Studying available implicit inputs in viewing behaviours which can be used to create user profiles in future TV services.

Transkript

1 Studying available implicit inputs in viewing behaviurs which can be used t create user prfiles in future TV services. Lisa Aspund 31 January 2010 Master Thesis in Cmputing Science, 30 ECTS credits Supervisr at CS-UmU: Thmas Jhanssn Supervisr at Ericssn Research: Jakim Frm Examiner: Per Lindström Umeå Universitet Department f Cmputing Science SE UMEÅ SWEDEN

2 2

3 Abstract The latest televisin technlgy gives the viewer a large amunt f media cntent t chse frm when deciding what t watch. Recmmendatin systems ease the task by using the viewers previus references, fund in a user prfile, and basis recmmendatins upn that. The user prfile can btain infrmatin by bth explicit and implicit inputs. Explicit inputs are given directly by the user, e.g. answering questinnaires and implicit inputs are indirect actins e.g. buying a mvie. But since explicit inputs takes up the viewer s time, implicit inputs are in that sense better t use. It is crucial understand the viewers by asking them abut their pinins. That was dne thugh a survey as well as interview sessins. T be able t track and understand implicit inputs in viewing behaviurs, the cntextual surrundings are helpful and can be capture by using sensrs. The fund inputs were visualized in Flash. 3

4 4

5 Thanks There are peple that I want t thank given that they helped me ut in sme way r anther during this prject. First f all I want t thank my family and friends, whm gave me supprt and encuragement thrughut this prject and especially thse times I really needed it! I als want t thank my supervisr at Ericssn Research, Jakim Frm and all the ther emplyees at the TGU department, and my supervisr at Umeå University, Thmas Jhanssn. And I als want t thank the ther master thesis students at Ericssn Research fr inspiratin and making the lunch break a delightful event! 5

6 6

7 Table f cntents 1 Intrductin Outline f the wrk Prblem descriptin Task The users The gal Limitatins Backgrund TV technlgies and devices - nw and then Measuring TV habits Recmmender systems What is a recmmender system? User prfiles Implicit and explicit inputs Types f implicit inputs Prs and cns f using implicit inputs Prblems related t recmmender systems Different filtering methds t use in recmmender systems Cntent based filtering and Cllabrative filtering Hybrid recmmender systems Multidimensinal recmmendatin mdel Existing Recmmender Systems TiV Amazn.cm The wrk prcess Generate ideas Scenaris Survey Prttype Taking it ne step further User studies Interviews Results The design Menu Infrmatin Cnclusins, discussins and future wrk Remte cntrl actins Sensrs Light

8 7.2.2 Fd Camera Away functin Time spent watching specific channels Genre Tp lists Tp f the tps Yur tp list Mst viewed Scial viewing Fd Active viewing Statistics Recmmendatins Evaluatins Lgin functin Additinal functinalities Prblems and slutins A. Appendix A Survey: Answers and questins: B. Appendix B Implicit inputs generated during the brainstrm sessin C. Appendix C Scenaris n PwerPint slides t get a verview D. Appendix D Analyzing scenaris E. Appendix E Usability studies fr icns F. Appendix F Interview

9 1 Intrductin Fr the past years the technlgies behind the televisin sets have given the viewers new ways f accessing media cntents. Nt nly have the image reslutin and screen size given the viewers a mre pleasant experience but the viewers als have a large amunt f media cntent t chse frm. A negative side f this is it culd becme a difficult task t chse what t watch. Recmmender systems have the purpse f helping the viewers making their decisin. T understand what the viewers likes and dislikes, a persnal user prfile is created fr every viewer. The user prfile cntains infrmatin abut the viewer which in this case culd be previus mvie ratings r watched mvies. This infrmatin can be cllected by the system by either explicit r implicit inputs, r bth. Explicit inputs are direct interactins with the system e.g. rating mvies while implicit inputs n the ther hand are indirect actins frm the viewers e.g. buying a mvie. The implicit inputs prvided t the system are much harder t interpret because they might nt give a clear answer e.g. it des nt imply if the viewer likes the mvie r nt by just buying a mvie. But by using several implicit inputs and cmbine them tgether it is easier fr the system t interpret these inputs. The cntextual envirnment has rich infrmatin and culd be tracked by using sensrs in rder t understand the implicit inputs. Recmmender systems are used in different areas but mstly n the Internet. A media device that uses recmmender systems are the ppular device, TiV. The TiV gives the viewers the pprtunity t give ratings n the watched cntent and the recmmendatins are based n that. Anther cmpany, lcated n the Internet that gives recmmendatins is Amazn.cm. On this site it is pssible t get recmmendatins withut a user prfile because the system use bught items as input, e.g. if a custmer buys tw bks at the same time, when yu enter ne f thse bks the ther will pp up as recmmendatin, s called The persn wh have bught this bk have als bught these. It is als pssible t create a prfile in rder t get better recmmendatins which are based n that. T get a better understanding f the viewer s behaviurs when watching media cntent, I first cnducted a survey which was sent ut t a large grup f users and secndly, t get mre infrmatin I interviewed a handful f users. The results gave sme understanding f what implicit inputs that culd be used. The implicit input that was recgnised can be divided int three categries: Remte cntrl actins, time, and sensrs. These inputs were visualized by using the graphic tl Adbe Flash and phidges frm Phidgets Inc were added in rder t sense the cntextual surrundings. 1.1 Outline f the wrk The reprt has the fllwing utline: Chapter 2: This chapter describes the task and the gal with this master thesis. Chapter 3: The third chapter hld infrmatin abut the backgrund, which is divided int tw parts; the first describes TV technlgies. The secnd part gives 9

10 infrmatin abut the Swedish cmpany MMS which in sme degree tracks viewing habits. Chapter 4: In the fllwing sectin infrmatin f recmmender system can be fund. This sectin has been divided int six subparts. The first part describes recmmender system and is fllwed by infrmatin abut user prfiles, implicit and explicit inputs, and prblems t get a deeper understanding abut recmmender systems. There are als infrmatin abut different filtering methds and a descriptin f existing systems which uses recmmender systems. Chapter 5: This chapter cntains infrmatin abut the wrk prcess. Chapter 6: In this chapter yu can find the result f my master thesis. Chapter 7: This last chapter hlds infrmatin abut the cnclusins and discussins f the result and suggestins fr a future wrk. 10

11 2 Prblem descriptin 2.1 Task The new generatin f TV technlgies enables new pssible functinalities t enrich the viewers experiences. Nw it is pssible t get the digital televisin delivered by a bradband cnnectin, IPTV. IPTV is ne way f accessing cntent lcated n the Internet, which gives the viewers cntrl ver their viewing schedule. But since there are tremendus number f TV-shws and mvies, recmmender systems filtering the cntent based n previus users preference, t help the viewers deciding what t watch by narrwing dwn the selectin. The task is t shw hw implicit inputs can be gathered during a TV sessin. This will be dne by investigating hw peple act and what kind f behaviur they demnstrate in frnt f the televisin. When this is dne it is imprtant t lk int hw t capture thse actins and behaviurs. And als lk int ther systems which use implicit inputs t generate recmmendatins. The master thesis wrk will be cnducted at the Usability & Interactin Lab, a grup within a unit called Service Layer Technlgies. SLT is a part f Ericssn Research and is respnsible fr research in services, enabling technlgies, and prtcls fr cmmunicatin and cntent delivery, user and device management, service deplyment and design. This prject is a part f a larger prject cncerning recmmendatin system within Ericssn Research. 2.2 The users The target users are everyne watching TV by using IPTV cnnectins. They are in different ages and have different knwledge in technlgy. They all have interests in TV shws and mvies and they use the latest technlgy fr btaining thse media. 2.3 The gal The gal f this master thesis is t investigate available implicit inputs in viewing behaviurs which can be used t create a user prfile. The user prfile is the basis f the recmmendatins prvided by recmmender systems. The recmmender system culd be integrated int a set-tp-bx fr IPTV. 2.4 Limitatins The time limit set n the master thesis is 20 weeks, 40 hurs per week. The gal is nt t build a full wrking prduct but rather a prttype in flash. N data will be saved when using the prttype due t my lack f knwledge in databases. 11

12 12

13 3 Backgrund The aim f this sectin is t give a shrt descriptin f the histry f TV, and its technlgies. 3.1 TV technlgies and devices - nw and then In 1927 the first mving image was bradcast between Chicag and New Yrk, and cuple f years later it started t get mre cmmn t have a televisin set at hme. Nwadays viewers have hundreds f channel t chse frm and the picture is nw in clur (1)(2). Thanks t the digitalizatin f bradcasting TV it is pssible t view yur favurite prgram in High Definitin (HD) (3) (4). Televisin has expanded t include ther devices which wrk in cnjunctin with the televisin set. Such devices can fr example be the VHS, which enables the viewer t recrd shws r mvies that are bradcast n the televisin. But als enables the viewer t view recrded shws r buy mvies whenever the viewers have the time. The viewer can als watch a shw r a mvie many times and share it with friends and family. Over the last ten years the VHS has been replaced with the DVD, which is easier t handle because they are smaller, and can cntain mre infrmatin (5). The bradcasts have als changed in the last cuple f years, ging frm analgue t digital (3)(6). A single analgue channel can be transfrmed in t six digital channels which means there are ging t be mre cntent available (7). The digital televisin services can be delivered ver a netwrk infrastructure by using Internet Prtcl, s called IPTV (8). IPTV is mre flexible than the traditinal TV cntent because the user can access media lcated n the Internet, as Vide n demand. Vide n demand makes it pssible t watch shws and mvies whenever the viewers have the time t d s. They d nt need t fllw the schedule f the channels in rder t catch their favurite shw and during the shw it is pssible t rewind, pause, and stp and cntinuing t watch later. The pwer is in the viewers hands (9). In millin husehld used IPTV and are expected t grw 52.2% annually until 2012 (1). Nw when the TV netwrks prduce Internet based services, e.g. SVT Play, the viewer can access a lt f TV cntent via their cmputer r thugh ther digital televisin services e.g. IPTV (8)(1). The bidirectinal cmmunicatin in the system gives further visibility n TV viewing activities, e.g. it is easy t track infrmatin abut the viewer and their behaviur (1). The new generatin f TV sets has built-in sensrs which register if anyne is sitting in frnt f the TV. If nt, the TV will turn ff the TV screen in rder t save pwer. The TV sets, which are prduced by Sny, have the gal t decrease pwer usage and becming mre envirnmentally friendly. In rder t detect if anyne is in the rm it uses tw sensrs. The first sensr detects heat in the surrunding area and if n heat is registered, the TV set will g int standby mde. The secnd sensr tracks mtin and turns ff the system if n mtin has been detected during a 30 minutes perid (10). 13

14 3.2 Measuring TV habits At the present time there are nly a few cmpanies which cllect infrmatin abut what is watched when and by whm. In Sweden the cmpany MMS cllects the data which nly regards the ppulatin f Sweden. Since 1993 the cmpany MMS - Mediamätning i Skandinavien (Media measurement in Scandinavia) has prduced statistics f Swedish viewing habits. They cllect infrmatin frm a carefully selected panel which tgether has the same prperties as the ppulatin f Sweden. Fr instance, if 5 % f the Swedish ppulatin is students, 5 % f the panel shuld als be students. The panel cnsists f 2600 participants which implies that ne persn represents abut 3000 peple (11). This suggests that errrs in the calculatin might be fund but accrding t MMS they take the number and adjust it with a weight factr if necessary. The participants have a bx in their hme which is cnnected t the TV. Every time the participants jin in r leave the TV they have t use a remte cntrl t lg in and ut, making it pssible fr the system t knw wh is watching. If the participants have guests ver, they t shuld lg int the system and prvide their gender, age, and ther imprtant infrmatin (12). The participants already prvide the system with all necessary details at the beginning f the panel perid s they can easily lg in and ut by just pressing buttn (12). The infrmatin prvided t MMS gives a gd generalizatin f what peple in Sweden watches, e.g. a male between years ld watch a certain channel at a certain time (12). 14

15 4 Recmmender systems 4.1 What is a recmmender system? Peple receive and give recmmendatins n a daily basis. If yur friends, clleagues, family members r ther peple yu meet find an item they like and that pleases them they prbably pass this infrmatin n. If peple are ging t buy a prduct but d nt knw which brand t chse frm, they ften cnsult their friends r peple wh have sme experience in that area (13). They might als read reviews in the newspaper r nline t fund ut what specific prduct that will best suit their requirements. Recmmender systems are a special type f infrmatin filtering system (14)(15)(13). The beauty f a recmmender system is that it tries t find the best recmmendatin suited fr yu. T be able t give suggestins and create recmmendatins the system needs users input and their previus preferences (13) and the mre the system knws abut the user the better these suggestins will be (15). Accrding t (16) 64 % f users are willing t give their remarks abut a shw, if they receive a persnal recmmendatin frm the system, while 57 % said that they were cmfrtable with prviding their remarks abut shws which wuld result in recmmendatin fr ther users. Withut any reference t what the users like and dislike it will be hard and an almst impssible task t give recmmendatins that are useful fr the users. Recmmender systems can be fund in any media, where the purpse is helping the user t find new unexplred items. Recmmendatins are a useful tl when there are several hundred items fr users t chse frm. In sme recmmender systems peple prvide recmmendatin as an input, which the system then tries t interpret and direct t suitable users (13). Fr example, if I have read a bk that I really like, I can share this infrmatin as an input t the system which directs it t ther users. T create a recmmender system it is imprtant t have user prfiles and input data, the input can either be implicit, explicit r bth. These terms will be described in the fllwing sectins. 4.2 User prfiles The available amunts f digital multimedia cntent, alng with the easy access d nt make it easy fr the viewer t find the mst interesting cntent. A slutin is t create a user prfile fr every viewer. The user prfile then hlds infrmatin abut the user, e.g. what mvies he r she likes, r seen mvies, and base recmmendatins upn this infrmatin (7) (17). In rder t btain effective usage f the user prfile it is imprtant t keep all f the infrmatin in the prfile up-t-date, because t give gd recmmendatins it might be crucial t knw what mvies the viewer already have seen (17). What infrmatin t include in a user prfiles depends n the type f system but mst recmmender system cllect infrmatin abut users ratings. The infrmatin abut the users can be prvided direct r indirect e.g. the user assign its hme telephne number, the system then knws where in the cuntry the user lives and in sme cases a specific area within the city (17). 15

16 4.3 Implicit and explicit inputs Explicit and implicit are tw ways f prviding the user prfile with infrmatin (7). Accrding t the dictinary (18) implicit is explained as Capable f being understd frm smething else althugh unexpressed and explicit has the explanatin Fully revealed r expressed withut vagueness, implicatin, r ambiguity: leaving n questin as t meaning r intent. With this in mind, explicit inputs culd be answers t questinnaires r mvie ratings while implicit inputs n the ther hand culd be time spent n a specific questin r buying a mvie (19). Tday there are several systems using explicit input t gather infrmatin t create user prfiles, e.g. TiV (see 4.6.1). Implicit inputs are nt used as frequent as explicit inputs but are used by cmpanies n internet like Amazn.cm (see 4.6.2). They use bught items as implicit input, and base recmmendatins n that, i.e. when viewing a bk n their site, there are available infrmatin abut items the custmer wh have bught that bk als have purchased (20). Implicit inputs are als used t gather infrmatin abut hw peple act when viewing web pages. These can be used fr analysing the usability f the web page. Fr example, it indicates unused links and places the user abrt their purchase sessin (20) Types f implicit inputs Peple encunter implicit interactin every day withut nticing. Cmmunicatin between tw peple can be cnducted entirely by implicit interactin by using the bdy as a tl. Smene passes yu by, lking int yur eyes and smiling and in return yu smile back. If yu d nt bther asking, yu have n straight answer why the persn smiled twards at yu. It culd be that the persn liked yur appearance r fund smething funny abut yu (21). Every interactin the user perfrms with the system can be used as an implicit input (22). The input t cllect and apply depends n inputs available and mre imprtantly the purpse and the gal f using the inputs. Fr instance, in an ffice envirnment typing speed might be a gd indicatin f their cmputer skills (20) but time spent reading an article n a webpage may shw amunt f interest (21). T get mre infrmatin abut the behaviurs f the viewers and draw cnclusins upn them the system needs mre infrmatin abut the surrunding envirnment. T be able t extract this cntextual infrmatin, sensrs are ften used (23) Prs and cns f using implicit inputs Implicit inputs are a gd way f gathering infrmatin abut the user since they d nt need the same effrt frm the user as the explicit nes (24)(19). The users d nt get disturbed during the activity which is in fcus and can prceed with their nrmal pattern (19). Since implicit inputs are cllected inactively and are therefre mre uncertain, e.g. just because yu are buying a mvie des nt imply anything abut whether 16

17 yu like the mvie r nt. It is therefre a quite difficult task and needs a lt f effrt t interpret thse inputs t prvide a user prfile which fits the user. Therefre they are nt cnsidered as trustwrthy and accurate as when explicit inputs are being used (25). One way f imprving the accuracy f the recmmendatins is thrugh using multiple implicit inputs and cmbining them, in rder t create persnalized prfiles. If systems use multiple inputs, the cmputatinal cst f string and prcessing will increase with the number f inputs used (22). The recmmendatins culd be imprved even further by cmbine the implicit inputs with explicit input. This culd give the system sme clues as hw t interpret the implicit inputs by using explicit inputs as measurements (19). When the system uses explicit input, viewers might nt be hnest in the infrmatin given abut themselves t the system. They culd give gd reviews t thse mvies and shws that they want t be assciated (24)(19). Fr instead, if the user likes the TV-series Midsmer murders but has heard frm his friends that it is an ld flks shw, he might nt give the shw a high rating because the shw des nt fit his persnality r the persnality he wants thers t have f him. This prblem will nt ccur when using implicit inputs because the system uses infrmatin given by the user uncnsciusly (19). Mre data abut the viewer can be cllected when using implicit inputs because when the system nly uses explicit inputs the viewers are nt willing t give rates n all cntent watched (7). This culd result in mre accurate recmmendatins since the system have mre infrmatin abut the viewer. One advantage f using implicit inputs instead f explicit is that when explicit inputs are being used a prblem culd ccur if the user stps prvide ratings. Because nce a prfile has been generated, it is pssible fr users t take a free ride, and nly cnsume mvies recmmended by thers and prvide n recmmendatin themselves (13). 4.4 Prblems related t recmmender systems One prblem is that peple in general find it hard t accept recmmendatins if they d nt knw what infrmatin thse recmmendatins are based upn (14). It is therefre crucial t give a clear and pen view t the user f hw inputs generate utputs in the frm f recmmendatins. If the system has transparency, it will allw the user t refine and adjust inputs in rder t imprve their given recmmendatins (14). Recmmender systems als raise cncerns abut persnal privacy (13), since the recmmender system stres the viewers behaviurs and pinins abut certain mvies and TV shws. Peple might disapprve f that and be wrried that the infrmatin might be used in ther areas. The mre infrmatin the system has abut the user the better the recmmendatin will be, but this has t be weighed against the amunt f infrmatin the user is willing t give (13). 4.5 Different filtering methds t use in recmmender systems When the system has gathered all f the infrmatin needed it is highly imprtant t make use f it in the best way pssible. Since the system s purpse is t give recmmendatins there are algrithms available t srt ut the infrmatin 17

18 needed and cmpare it in a smth way t ther users. In this sectin fur different filtering methds will be described Cntent based filtering and Cllabrative filtering There are different methds t use t generate recmmendatins. The tw mst cmmn methds are Cllabrative Filtering (CF) and Cntent Based filtering (CB) (15). Cntent-based filtering is based n the crrelatin between the user prfile and items cntent (26). This means that if yu have rated The Simpsns highly and a shw similar t The Simpsns is The family guy, and yu will then get The family guy as a recmmendatin. Cllabrative Filtering is mre based n users prfile crrelatin (15). It tries t predict yur rating n an item based n what ther users previusly have rated the same item (26)(24). If yu and anther user have seen the same mvies and rated them the same, yu culd get recmmended ne f the mvies that he has rated highly. The secnd ne des nt take int cunt what type f mvie it is, what genre it can be categrized int but the first ne des s Cllabrative filtering The first recmmender system, Tapestry, used cllabrative filtering t take care f the filtering prcess (13). The cllabrative filtering algrithm is the methd mstly used by recmmender systems. Since it was the first and mstly used it can be seen as the backbne f recmmendatin systems (14). The CF algrithm can be viewed as a scial filter because it is mdelled after the scial prcess t get recmmendatin frm friends with similar taste n bks, mvies, and music t recmmend things that yu might like (14)(19). One prblem f using cllabrative filtering ccurs when a new item is added int the system. Since the recmmender system with CF relies n rating data t prvide recmmendatins, the new item will therefre nt be recmmended until a significant number f peple have rated it (26)(19) Cntent-based filtering A system that uses cntent-based filtering algrithms prvides recmmendatins t the user by analysing thse items which have been rated highly by the user in the past and finding similarities in thse items. The algrithm then calculates recmmendatins by finding new items that matches with thse similarities (26). In shrt, the cntent-based filtering algrithm uses the mvie attributes e.g. genre, cast, age, etc t create recmmendatins (24). Cntra cllabrative filtering, cntent-based filtering des nt have prblems with recmmending new items that are added int the system. Instead the prblems ccur when new users access the system. The user needs t rate a sufficient number f items befre the system can spt patterns and prvide suitable recmmendatins based n thse (26) Hybrid recmmender systems Sme systems use bth cllabrative and cntent-based filtering t prduce a prfile which then all recmmendatins will be based upn. This culd wrk ut 18

19 as fllws: Learning and maintaining user prfiles by using cntent-based filtering techniques and cmparing the prfile with thers t find thse with similarities which results in cllabrative recmmendatins (26). This will result in recmmendatin based n either the items that are scred highly against the viewer s prfile r are rated highly by viewers with similar prfiles (26). Since the hybrid recmmender system uses bth cllabrative and cntentbased filtering t prduce recmmendatins, it will nt suffer frm any s called cld starters, i.e. the system will nt suffer when new items r new users are added int the system Multidimensinal recmmendatin mdel Nne f the recmmender systems abve have taking any cncerns abut the cntextual envirnment enclsing the viewers. The multidimensinal recmmendatin mdel extends the traditinal user-item with cntextual infrmatin (26). Thse dimensins culd fr example be, time, place, persns, cmpanin, and s n. This means the user will be given recmmendatins based n a curtain cntext, e.g. the user wants t see a mvie in a mvie theatre n a Saturday night, and the mdel then recmmended mvies which gt highest ratings n weekends which were seen in mvie theatres (26). The rating assigned t a mvie need t be specified where it was seen, tgether with whm, and at what time. This culd als result in a mvie, a place and cmpanin as a recmmendatin, e.g. n Saturday, Harry Ptter tgether with yur mther (26). 4.6 Existing Recmmender Systems There are a lt f cmpanies using recmmender systems t prvide their custmer with recmmendatins and tw f them are described in this sectin TiV One imprtant device that has changed TV behaviur in the US cmpletely is the TiV system. TiV has the same functinality as a Persnal Vide Recrder (PVR) - als called Digital Vide Recrder (DVR) - system (27). A Persnal Vide Recrder uses a hard drive which makes it pssible t stre cntent nt it. The viewer is than able t interact with the bradcast shws. This means that the viewer can easily pause during a bradcast shw and cntinue watching it later. The viewer can als rewind and fast-frward TV cntent which enables the viewer t time-shift the bradcast cntent. In this way, the viewer can easily rewind t see a gal in ice-hckey nce again r fast-frward thrugh advertising (27)(25). The Persnal Vide Recrder als has the built-in feature f autmatically recrding cntent based n title, actrs, theme, rating, etc, and adjust recrdings if there have been any changes in the time schedule (27). Accrding t (28) the Persnal Vide Recrder has great value fr thse peple wh have a different time schedule frm mst f the ppulatin since they can watch shws and prgrams when they have the time. In 2006 there are nly abut 8% f the Swedish ppulatin that have a PVR system in their hmes and is increasing cnstantly t abut 30 % in 2008 (30). In the USA 27% f the husehlds have at least ne PRV and 30% f them had mre than ne (30). 19

20 TiV might be the mst well knwn PVR system and as mentined befre, it has the same functinalities as a regular PVR system has (28). TiV as well as PVR cnsists f a set-tp-bx. TiV uses an explicit seven-scale system t cllect infrmatin f what the viewer likes and dislikes which has the maximum f three thumbs up and minimum f three thumbs dwn (25). The nly implicit input the TiV uses t get an understanding f hw well the viewer likes r dislikes the cntent is if the viewer recrds a shw. The recrded shw will get ne plus in its ntatin (24). Accrding t (24) TiV has abut 100 millin ratings prvided by users fr ver 300,000 different TV-shws and mvies. TiV uses the cllabrative filtering methd t prvide recmmendatins t their viewers and is based n an item-item system. This eliminates the need fr keeping any infrmatin fr every viewer in the TiV server. Thanks t this structure, mst f the wrk is delegated t a number f clients and as little wrk as pssible is dne n the server side. The viewer can easily find new unwatched shws by using a different index, e.g. actrs, genre, theme, etc, and the TiV will calculate the prbability f hw well the shw will suit the viewer (24). The TiV cmpany has designed an easy interactive remte cntrl (31). Cmpared t mst f the remte cntrls existing in mst hmes, the TiV remte has large buttns with a clear label and sme even have clurful cartn icns which makes it easy t lcate them in the dark. The arrangements are lgical which makes them easy t lcate and use when navigating thrughut the several menus inside the system. The physical design f the remte has been given much effrt and thught which has prduced a peanut shaped cntrl (31) Amazn.cm Amazn is an internet cmpany that uses the custmer s previus preference t display items they might have higher interests. If the custmer uses the site fr the first time the default items that are shwn are their best seller prducts in different categries. The custmer can create an accunt which lgs purchases, and future recmmendatin are based n the user prfile. The user can als get recmmendatins with is based n infrmatin abut ther custmers purchases. When the custmer accessing an item, related items are shwn as recmmendatins. The relatins are that they have been bught by the same custmer. T get even better recmmendatin it is pssible t infrm Amazn.cm abut items yu wn and furthermre rate items (20). In this case, amazn.cm uses bth implicit and explicit inputs t gather infrmatin abut the custmer t create a persnal prfile. Since the explicit inputs are ptinal, this system aims at bth thse wh d nt want t put in extra effrt, except buying an item, t get persnalized recmmendatins and thse wh want give ut as much infrmatin as is needed t get as gd recmmendatins as pssible (20). 20

21 5 The wrk prcess T get a better understanding abut the subject in questin, I started my master thesis with a literature study. I fund a lt f interesting material abut the histry f televisin, hw it has evlved, and sme studies abut human behaviur cncerning watching TV. But mainly I fund studies and facts abut recmmender systems, algrithms that can be used, methds that have been cnducted and what inputs can be used as infrmatin t the prfile. 5.1 Generate ideas The next step in this prcess was t list every implicit input that can be fund. When the gal is t generate, refine, and develp ideas using a brainstrm technique is a gd way t start (31). There exist a number f different brainstrm techniques. A Basic Brainstrm sessin can have the fllwing apprach; write what is n yur mind n a piece f paper and put it n the table. D this fr a cuple f minutes and then cllect the ntes, pair duplicates and discuss what is written (33). This methd generated numerus f different ideas f inputs and sensrs that culd be used by the recmmender system t create prfiles. (All f them can be viewed in Appendix B) Mst f them were mre realistic t use but sme were almst impssible t cnduct. Thse inputs culd be srted int three categries: Remte cntrl actins, sensrs, and actins. The first categry includes inputs which are cnnected t the remte e.g. changing channel, lwering the vlume, etc. The secnd categry included inputs which culd be lk upn as high tech e.g. eye tracking, RFID-tags, etc. And the third categry included thse inputs which culd be related t actins cnnected with watching TV e.g. watching a mvie which has been bught, r eating while watching the mrning shw. 5.2 Scenaris T see hw thse inputs frm the brainstrm sessin can perate in situatins surrunding TV viewing I made a number f scenaris which were based n tw r mre inputs. The gal with these scenaris was t get a better understanding f what input wuld be necessary t use in rder t get a specific recmmendatin. Every scenari was analysed in the terms f technical supprt, Is this pssible?, what des it imply, and is it pssible t jin the scenari tgether with anther t get strnger and mre bvius meaning. In rder t put scenaris tgether, an verview f all scenaris was crucial (see Appendix C). This was dne by putting ne scenari n ne PwerPint slide and printing it ut. On each slide write the three analysis pints and afterwards try t grup them tgether. These scenaris raised a bunch f questins abut their riginality, e.g. d these situatins exist and what are they meaning. 5.3 Survey The nly way t get answers t thse questins frm sectin 5.2 and t get a better understanding f hw peple acted in varius situatins was t ask the viewers. Surveys are a tried methd t gather users pinins as well as demgraphic data. Many surveys start with persnal questins e.g. age, and gender, which is useful fr putting the questinnaire in a cntext (31). When designing a survey it is als imprtant t think abut the rder f the questins and put questins that handle the same tpic tgether. Surveys are a gd tl 21

22 t use if yu want t cllect infrmatin frm a large grup f peple but it is highly imprtant t reduce misunderstandings by bth prviding clear instructins n hw t cmplete the questinnaire and the questins need t be clearly wrded. If thse cnditins result in n misunderstandings then the data will be efficiently analysed (31). The survey cntained seventeen main questins abut viewers habits surrunding bth TV and mvies. In ttal there were 51 questins t be answered. The reasn fr putting this survey tgether was t find ut what the viewers thught, felt, and hw they acted and reacted t specific situatins. The survey was handed ut t students at Umeå University all f whm were studying in a master prgram f engineering in Interactin and Design. Nte that mst f the participants fall in the same age span and their technical interest can be cnsidered higher than average due t their chice f study tpic. Since it is a technical educatin, mst f the participants are male. The participants live in the same cmmunity which might have affected the result. The survey was created nline by tls prvided by Ggle.cm. Ggle.cm als cllected the answers in a spreadsheet which made it easy fr a quick verview f the returned answers. The survey was handed ut thrugh with an attached link t the nline survey. The survey went ut n the 7th f April and the answers arrived between 7th and 21st f April were taken int accunt. The whle survey can be fund in the Appendix A. 5.4 Prttype T easily shw hw thse inputs discussed abve culd be used t create a user prfile, I used the graphic visualizing sftware Flash. The sftware is an inspiring tl t use t create interactive experiences. It is easy t transfrm images t animatins and the user can als create thse animatins by using the bjectbased script language actin script (34). The latest versin f Flash uses actin script 3.0 which is based n ECMA-script, the internatinal standardized prgramming language fr scripting (34). The idea was t make it like a TV with all the functinalities a TV has and the flash prject wuld made it easy t get access and see statistics generated by inputs. T make it as authentic as pssible it is necessary t shw mtin pictures n it. Therefre I tk clips frm YuTube and integrated them int the flash prject. The infrmatin needed t be easily accessed but nt destrying the feeling f watching the shws n an rdinary TV-set. The infrmatin was therefre displayed n a sliding windw which culd be easily accessed and hidden. Mst f the actins were als prgrammed in such way it culd be activated by using a remte cntrl. The remte cntrl used in this prject was prduced by Micrsft. 5.5 Taking it ne step further An expansin t the flash prject which wuld make it pssible t trace the cntextual envirnment, I used RFID-readers, RFID-tags, web camera, micrphne, and a light sensr. Phidget Inc prvides a set f different sensrs called Phidgets. These Phidgets are a set f plug and play building blck that uses USB technlgy cntrlled frm the cmputer. By using a rbust API, it is pssible t fr users t use their prgramming language f chice (35). Languages supprted by the Phidgets are Actin Script 3, Java, C#, MATLAB, just t mentin a few (35). These devices were easily implemented and integrated int the flash prject, thanks t their actin script supprt. One idea was using fd as an input which cmes frm an early hunch that TV viewing 22

23 and fd have a clse relatinship, and this was later shwn t be s (Appendix F). Each type f fd was given a unique RFID-tag s the system culd easily recgnise which type f fd was present. The light cnditins in the TV-rm were als tracked by using a light sensr. In this future scenari a camera and a micrphne were set with the functinality t sense if anyne is present in frnt f the TV. 5.6 User studies It is imprtant t present yur ideas fr the user in an early stadium f the prject because the users will generate imprtant feedbacks. It is easier t d changes and take steps back (31). When almst half f the master thesis had passed by, I shw the flash prject t a small grup f peple with the purpse t get feedback and discussing ideas. The meeting generated in changes and ideas hw t evlve the prject further and present the infrmatin even clearer. When the majrity f the grund elements had been created it was imprtant t get the right icns fr the right input since usability is a key requirement. T get the best icn t symblize each event, several icns fr the same event were illustrated and demnstrated t five peple (see Appendix E). These peple were asked questins abut the clearness f each icn, and if they understd what the assciated event were. They als had t answer what icn they thught mst bviusly assciated with an event and if they thught nne fitted, what they thught the icn shuld lk like. The result was then applied t the flash prject. 5.7 Interviews Finding ut mre abut the target grup and hw they thught, felt, and what they wanted, I cnducted a cuple f interviews. Befre ding interviews it is imprtant t plan the interview which invlves develping the questins (31). There are things t cnsider when creating the questins: Try nt t have cmpunding sentences because it is easier t recrd, and als keep the questins neutral. Fr example, d nt ask Why d yu like this ne? because it assumes the persn likes it; ask instead What d yu think abut this ne? (31). The interviewees had 15 main questins and the entire interviews tk place between 1st and 20th f September. The interviewers were in ttal five peple aged between 21 and 56 years and mst f the interviews were cnducted ver the phne. The whle interview, can be fund in the Appendix F. 23

24 24

25 6 Results The inputs I decided t wrk further with are results f the survey, interviews, and scenaris made earlier. The inputs used and visualized in a flash prttype are as fllw: Remte cntrl actins: Play Pause Recrd Stp F. Frward Rewind Channel +/- Vlume +/- Mute Channel numbers EPG Time related: Sessin time Channel Genre Shw Sensrs: Mtin Sund Light Fd The devices that detect these inputs are: Remte cntrl Camera Micrphne RFID-reader RFID-tags Light sensr 6.1 The design The flash prject uses tw sliding menus which cntains all infrmatin prvided by the chsen implicit inputs. Thse can be accessed either by using the muse device r the remte cntrl. 25

26 Figure 1: A screen sht f the design. The tw menus can be lcated t the right, infrmatin, as well as at the bttm f the screen, Menu + One f thse sliding menus, named +, can be lcated in the bttm f the screen. In this menu there are nine icns which all are used t illustrate the cntext surrunding the TV set. When an icn is in a black clur and lks empty, it implies it is nt registered at the mment. The first icn that can be seen is a persn register which indicates hw many viewers there are in frnt f the TV. At the mment the figure nly changes between 0 and 1 and are trigged the away functin (see sectin 7.3). Figure 2: When the + menu is activated it eases ut and the whle menu appears. Bth the light and the fd symbls are inactive due t there have nt been clured. Figure 3: N ne is watching Thereafter is incandescent lamp icn which shws the light cnditins with the help f the light sensr. The purpse with the sensr is t bserve the light cnditin in the surrunding envirnment when mvies r shws are being watched. The light icn has fur states; dark, medium dark, medium light, light, and the icn visualizing this by changing the clur inside the lamp. When den sensr senses a dark rm, the clur inside the lamp turns int a dark clur which represents an unlit lamp. But as fast as the sensr register the rm is lit up; the clur inside the lamp changes t yellw which symblized a lit lamp. 26

27 Figure 4: The lamp icns fr states. The first ne symblizes a dark rm and the last ne symblizes a rm full f lights. The eight fllwing icns are all f the same type; fd. The types f fd presented shuld nt be a standard rather be persnalized. The systems are nt suppsed t detect the fd itself but rather the presence f plates, cups, glass, and cutlery, and in the analyses prcess understand if it is bread, candy, dinner, r cffee. But t make it easier t visualize every item have been assign a specific RFID-tag t them which has a distinctive value. The flash prject then recgnises the type f fd present by cmparing the RFID-tags value and the right item appears in the + menu. Just as in the real wrld, many types f fd can be put n the table at the same time, s the + menu can have several types f fd active at the same time. Unfrtunately nly ne RFID-tag can be registered by the RFID-reader which means fr every RFID-tag present, ne RFID-reader must have been cnnected t the system. Whenever a fd are being detected by the RFID-reader the icn will changed frm being empty-like t shw a mre detailed versin. The different fd icns lcated in + menu are: Bread: The bread symblizes all types f bread and sandwiches. Candy: The candy represents nt nly candy but als chips, ppcrn, cheese ddles, chclate, peanuts, and all snacks imagined. Tea: This symbl represents all kinds f ht beverage cnsumed in a larger cup, e.g. ht chclate, tea, sup. Cffee: The cffee symblizes all types f cffee. Cupcake: This symbl represents all kinds f cakes, ckies, pastry, and deserts. Plate: The plate represents the three main dishes, breakfast, lunch, and dinner. Wineglass: This symblizes all types f alchlic beverage. Glass: The glass symblizes all kinds f nn alchlic beverage such as water, juice, lemnade, sft drinks, etc. Figure 5: Shws all f the fd symbls in active state. The appearances f all f the Fd icns have been prduced with help f participants (see sectin 5.6). 27

28 6.1.2 Infrmatin The ther sliding menu can be lcated n the right side f the screen and named Infrmatin. In this menu prvides inputs and visualizatin f hw they can be used. This sliding menu has five main menus; Prfile, Tp lists, Recmmendatins, Statistics, and Camera. All f them can be accessed either by using the muse device r the remte cntrl Prfile The prfile menu has infrmatin f hw lng the viewer have watch, channels, genres, and TV bth in ttal as well as the present sessin. The amunt f time spent n channels and genres are bth shwn as a percentage based n the ttal viewing time and are visualized in an active status bar and numbers. The percentage updates every secnds. Figure 6: Shws hw the infrmatin menu eases ut when it is activated and the infrmatin under Prfile is being viewed Tp lists In this menu the viewer can fund six different tp lists based n different criteria. These are; Tp f the tp, Yur tp list, Mst viewed, Scial viewing, Fd, Active viewing. All f thse categries had been divided int ne tp list fr TVshws and ne fr mvies. The tp lists illustrate the mst ppular shws and mvies fr thse six categries mentin earlier. A single categry des nt necessary depend n ne input, it culd be a mixture f tw r mre, depending n what the categry is and available inputs. Result frm the survey prved that a mvie watched several times is thught as a gd mvie frm the viewer. 28

29 Figure 7: Shws the appearance f the tp lists Recmmendatins In Recmmendatins there are recmmendatins shwn fr bth TV shws and mvies. The pint with this is t shw hw the usage f different input can generate in different recmmendatins. In this prject, the viewer has the pprtunity t chse what the recmmendatins shuld be based n. 29

30 Figure 8: An example f hw recmmendatins culd be based upn the inputs chses by the viewer Statistics When all sensrs and actins have been registered the values can be fund under Statistics. The values have been placed inside a graph, with the time spanned during a week. At the mment there are seven inputs shwn in the graph and every input has its wn clur. Each input has a checkbx which makes it pssible t active and inactive the respective inputs graph. It is pssible t view the statistics n a daily bases by chsing a day and the additinal graph fr that day is shwn beneath. In this day graph it is pssible t see infrmatin per hur by accessing the hur f chice and the in the infrmatin is shwn in a bx n the left side. 30

31 Figure 9: A graph with all input values n a week bases. Figure 10: It is pssible t get a clse lk daily by simply chse the preferred day. T make it easy t cmpare and bserve the inputs it is pssible t activate and inactivate inputs by using the checkbx next t it Camera Under this headline the camera image is displayed as well as the micrphne s input vlume. The camera has bth a blurry and yellw-clured filter which is a result f peples unease f being watched. The purpse f the camera is t be used as mtin detectin in rder t sense any mvements in the rm. The micrphne has almst the same purpse, but instead f mvements it senses 31

32 sund and nise in the rm. The gal it is t detect if anyne is arund watching and if n mtin and nise is detected the away functin will be activated. Figure 11: Under the camera it is pssible t view the image the camera register and als bserve the amunt mvement under the image. The nise measurement can be sptted n the right side f the camera image. Figure 12: When n ne is arund and n nise is detected, the status changes t away. All f these main menus can be accessed by the remte cntrl. 32

33 7 Cnclusins, discussins and future wrk In this sectin I will discuss feature used in this prttype and hw the prject can be imprved in the future. 7.1 Remte cntrl actins This is the mst bvius input the viewers prvide the system with. The inputs frm the remte can be cmbined tgether with ther inputs in rder t interpret them easier. Because if the viewer pause during a mvie it might nt give the system the infrmatin f why the actin ccurred, s it might be easier fr the system if it als knws that the viewer left the rm during this event. Remte actins culd als be used in rder t understand f hw imprtant the cntent is fr the viewer. Because their behaviur befre and after watching a cntent culd be different if they like it r nt. Fr example if the viewer directly ges t a specific channel and watches the whle cntent and afterwards turn ff the TV-set, that might imply the viewer like the cntent. But if the viewer changed channel multiple times, als knwn as zapping, befre deciding what t watch, culd this imply dislike f the cntent? It is difficult t understand why viewers have different behaviur and what their intensins are. Therefre by ding tests it will hpefully answer the questin abve and als gives a better understanding f the viewers behaviurs. 7.2 Sensrs In the flash prject nt nly existing inputs were discussed and used but als inputs which are based upn future features that are fund in peple s everyday life. These sensrs have the purpse f sensing the cntext in which the viewers are lcated in, in rder t give a richer recmmendatin basis. The infrmatin culd then be used by the system t get a deeper understanding f why certain actins are prefrmed. Fr example if the viewer pauses during a mvie, in what way shuld the system interpret that - des it imply like r dislike f the mvie? This questin is hard t answer even with additinal sensrs but the sensrs can give clues and hints f what the viewer thinks abut the mvie. The sensrs can sense if viewers have left the rm, r if there is a lt f mtin befre and after the event. T be able t sense the cntext, the prject used a kit frm Phidget Inc which made it pssible t simulate technlgies nt yet available fr everyne Light The purpse with the sensr is t bserve the light cnditin the in the surrunding envirnment when a mvie r shw are being watched. It is nt s clear f hw t use this input and if it is f any use by itself. Fr instance try t understand if there are different judgments f the cntent if it is being watched in a dark rm cntra a light up rm. Des the input have different meanings fr peple, sme always watch mvies in a dark rm but smene d it nly when there are smething special abut the mvie. But by cmbine this sensr with thers nes might give useful inputs t the system. Fr instance if the user have access t bth TV-set and a prjectr thse times the prjectr is being used the rm might be darker in rder t get a better 33

34 picture. If the system had the knwledge f this it might be easier t interpret, e.g. when the prjectr is being used the rm is darker Fd In the future it culd be pssible t track even mre items present in the cntext surrunding the viewers and use them t prvide recmmendatins. The gal f using these inputs in this prject is nly t shw what type f inputs might be able t be used in the future. Since it is, accrding t the interviews, cmmn t eat and drink in frnt f the TV, it wuld be interesting t track all f the eating and drinking events since mst f thse events trigger a viewing actin. These eating and drinking events can include breakfast, dinner, cakes, candy, snacks, tea and the list can g n t mentin all kinds f fd. Accrding t the interviews, peple tend t have a matching eating and watching behaviur, e.g. a persn always eats breakfast during the mrning shw. This suggests that recmmendatin can be given depending n whether there is fd present and in that case, the type f fd. Fr example when the type f fd is ntified by the system, it culd shw recmmendatin based n what ther viewers watched when eating this type f fd. The hard part is t recgnise what type f fd is present n the table. One way is t have bject recgnitin where the system analyzes the bject present and tries t determine what bject it is r t use a smart table which senses the item placed n it. These techniques culd be simulated by using RFID-tags and RFIDreaders prvided in the Phidget kit. In the real wrld it is extremely hard t track fd by using RFID-tags because it is difficult t analyze what it is in the glass, r n the plate. Just because a cffee cup is placed n the table, des nt mean it is cffee in it. The fact that peple are different and d nt eat the same things which means that the type f fd presented in this prject d nt suit everyne. Sme d nt drink cffee and thers never eat candy r ther types f snacks. Eating behaviurs als is a changing factr amng peple; sme eat every meal in frnt f the TV while thers never d s. A way f getting the types f fd t be persnalized is t create the list f fds after a specific fd have been detected a cuple f times. In this prject all types f snacks have been gruped tgether but if the system senses that different types f snacks are being used in different ccasins, e.g. eating candy when watching idl and ther TV-shws but eating ppcrn when watching a mvie. These culd then be separated int tw types f grups, fr example if the recmmendatins shuld be based n the kind f items n placed n the table and the systems senses ppcrn, the recmmendatins shuld mstly be mvies Camera The camera used in this prject is a basic web camera, which is cnnected t flash and is implemented with actin script in rder t track mtins made in frnt f the camera. The mtin sensitiveness is easily mdified in the actin script, this culd then later be changed depending f different cnditins e.g. light cnditin in the rm, the size f the rm. Unfrtunately the mtin detectin can register any light changes, even if the sun ges behind a clud culd be register as mtins. The image which mtins are based upn, is displayed 34

35 under Camera, and has been given blurring and yellw-clured filters. The idea with this is t shw that there are actins and mtins in frnt f the camera, but withut the uneasy feeling f being watched. The blurring filter makes it almst impssible t recgnise wh is in frnt f the camera and the yellw-clured filter makes the image seem unreal, and cartn-like. The camera can be used in situatins ther than thse already mentined. It culd be interesting t link mtin with ther actins e.g. when there are several viewers detect if smene leave the rm when the mvie r shw has been paused r des smene leave the rm withut pausing? Accrding t the survey, viewers used the pause actin whenever they left the rm and did nt want t miss ut n the mvie. This infrmatin can be used t mdify the persnal prfile. S if the system registers that the pause buttn has been pushed and smene leaves the rm, the mvie shuld get a slightly better review then thse mvies which are nt paused when smene leaves. A further extensin f the camera is t use heat r face recgnitin t be able t register hw many viewers there are in frnt f the TV. This infrmatin culd be used t weight the degree f mtins and nise frm the viewers. It is reasnable t draw cnclusins that the mre viewers there are, the mre nise and mtin will be detected, that might nt be a result f their dislike f what is being shwn. The number f viewers present can als be used t set the tp lists fr scial viewing and thereafter base recmmendatins n this infrmatin. One ther further functin f using a camera is t detect where in the rm the persn is sitting. This culd be perfrmed by using a heat camera t sense were the viewers are sitting. Since the interviews revealed peple tend t have a favurite place in the TV rm, this infrmatin culd be useful by the system when the husehld cnsist f mre than ne persn. Fr instance, if there are tw viewers using the same system, ne prefers t sit in the sfa and the ther ne favurite place is in the armchair. If the system detects smene in the sfa, this infrmatin can be used t give recmmendatins based n that place, the sfa. One prblem by using cameras is the viewers feelings and pinins f being register by a camera. The questin was asked in the interviews and mst f them did nt bther as lng the camera nly register mtins and did nt save and stre the data. Given this, the camera in this prject has tw filters which mdifies and twists the image with the ambitin f create an unrecgnisable image. Furthermre, t give the viewer the pprtunity f see the image used by the system may perhaps be easing and culd place mre cnfidence in the system. Nt nly in thse functins using the camera but an verall judgment f the system. 7.3 Away functin The away functin has the fllwing functinality; all updates, e.g. percentage f genre watching, are frzen while this functin is active and the away cunter begins. The functin has the purpse f nt cunting the shws r mvies shwn n TV during the away perid. In rder t create a prfile by nly using implicit inputs, it is imprtant t knw whether anyne is sitting in frnt f the TV, mainly 35

36 because if the system des nt take this infrmatin int accunt, shws and mvies might be added t their prfile despite that they have nt been watched. The away functin depends n tw sensrs; Mtin sensr and Micrphne. The mtin sensr uses a camera which detects if mvements ccur in frnt f the camera. Mst recently the new generatin f televisin is using mtin detectin t sense if anyne is present in the rm in frnt f the TV. That implies that the technlgy can be used fr mre than ne purpse. Nt nly is the gal t turn ff the system if n mtin is detected but als t ntify the system that n ne is arund and the TV is switched ff. Cntra the energy-saving televisin mtin detectr, the away functin shuld nt wait until 30 minutes has passed befre the system starts t register that n ne is arund e.g. shut dwn the TV. Instead just a cuple f minutes shuld past by befre the away functin starts, this t prevent the away functin triggered when smene is sitting abslutely still. By using an additinal sensr, the micrphne, it will increase the certainty f an empty rm. Of curse the micrphne culd register sunds which are nt frm humans r are taking place in ther rms. It is pssible t manipulate the sensitivity f the input f the micrphne t ensure sunds nly cme frm the TV rm. The registered input value that the micrphne detects is under Camera. 7.4 Time spent watching specific channels The channels are used in rder t see what channels the viewer watches the mst and als t understand what channels the viewer has access t. It might nt be surprising that different channels attract peple f different ages, genders, interests, plitical views, etc. This knwledge culd als be used in rder t create prfiles, e.g. if the viewer watches the Disney channel it prbably indicates that the viewer might be a child, r if the viewers have access t Eursprt, Viasatsprt, tv4 sprt, they prbably have an interests in sprts. But having access t channels and watching them are tw separate things. A specific channel culd be included in a large package f channels but the reasn fr rdering just that package had nthing t d with specific channel. That is why it is als imprtant t measure hw ften the channels are being watched. 7.5 Genre T understand a bit better what type f genre peple watch, the system als registers hw much time spent n a specific genre. A persn with an interest in sprts might nt have the typical sprts channels but instead watch sprt at every pprtunity prvide by the channels the persn have access t. Or in the ther hand a persn might have access t a number f sprts channels but never watch them. In rder t learn mre abut the viewer, analyzing this might give sme clues abut the persn, e.g. des the persn nly have the extra sprt channels because it gives a certain status r gives a certain persnality which the persn want t be cnnected t. It will nt nly be registered when the viewer watches live TV, e.g. the shws which are aired n a specific channel at a specific time, but als when the viewer watches TV n demand e.g. the viewer decided then t watch shws, but they did nt have t fllw the TV schedule. This means that when the viewer watches a shw n demand, the genre it represents increases but the since the viewer des nt watches a specific channel, nne f the channels increases. 36

37 7.6 Tp lists The infrmatin the inputs prvide t the system culd result in Tp lists which are based n different cnditins. A single categry des nt necessarily depend n ne input; it culd be a mixture f tw r mre, depending n what the categry is and available inputs Tp f the tps This tp list is based n all the ther tp lists with the purpse f prviding a list f thse shws and mvies that are placed highest n mst tp lists. It culd be lked upn as a summary f tp lists, which cunts the psitin f shws and mvies in ther tp lists and then summarizes it t ne list. The gal f this list is t be as well fitted t the viewers tp lists as pssible Yur tp list This tp list is created by the viewer and it is pssible t change the psitin f the shws and mvies by mving them up and dwn. In the beginning it might be gd t have the system guessing the viewers tp lists instead f leaving it empty until the viewers create the list. Since all viewers are different sme might fill in the tp list the first day but many might never d it. If the system guesses the tp list, the viewers might want t change it early n because they might find the fact that the system has filled in the list annying and the list prbably d nt fit the viewer's list. This is the nly explicit input used in this prttype. The interactin required in rder t add shws and mvies t this tp list shuld be an easy task t perfrm. By using the remte, it shuld nly take a few actins t add t it e.g. the pssibility t directly add the shw t it when the shw is being watched Mst viewed As the name f this tp list indicates, this list rates shws and mvies which are watched mst ften. The number f times watched is displayed right by the name f the shw/mvie. The main questin with this categry is; hw much time shuld have elapsed n a shw r mvie befre they are cnsidered watched? It is n pint in nly cunt thse shws which are being watched frm the beginning t the end because viewers might tune in a bit late bth in the beginning and when the cmmercial ends. One way f slving this is t use a percentage t measure if the shw r mvie shuld cunt as watched r nt. This means, if the percentage is set t 60, every shw and mvie which is viewed lnger than 60 % f the ttal time is cunted as watched. This is nly a preference fr thse shws and mvies watched n live TV because if the viewer des nt watch a whle shw r mvie which is always available, the cntent shuld nt be lked upn as being watch if nly 60 % f the cntent is watched Scial viewing This categry rate shws and mvies which has been viewed by mst peple. At the mment, the system des nt detect hw many peple there are in frnt f the TV, but this culd be dne by lgin functins mentin in sectin

38 7.6.5 Fd The purpse f this categry is t rank shws and mvies depending n the amunt f fd actins that has been sensed during the viewing sessin. The number f fds is cunted every time a type f fd is sensed. If a specific shw is viewed multiple times, every time sme type f fd is sensed, the ttal number f fds fr that shw will increase by ne. This implies that every shw and mvie must have a fd attribute attached t them, which hlds the type f fd but mst imprtantly hw many fd actins have being detected. The gal with Fd is t see if it is pssible t link what peple are eating with specific shws and mvies Active viewing This is a categry that be used t rank shws and mvies with inputs regarding the degree f fcus the viewers have n the cntent. Since there are n inputs that register the viewer s fcus, a mixture f mre than ne sensr culd make it pssible. These sensrs culd include; mtin, vice, zap rate and ther remte cntrl mtins. If all f these inputs have a relatively lw value it culd give the impressin f a high fcus n the cntent. There are risks f using thse inputs t create this tp list because if there is almst n mtin, sund, and remte actins it culd mean that the viewer has falling asleep r perhaps the mtin detectin has been set t a high sensitivity which means every change in the light cnditin is being register and n ne is arund. This implies that the viewers have nt fcus n the TV but the system register ttal fcus. A slutin fr this dilemma is t use an eye tracker, which tracks if the eye is pen r clsed but als where the fcus f the eye is. The system culd then easily track where the viewer has the fcus. But the viewer might nt like t be mnitr in that way and als it is prbably difficult integrate this slutin int the TV-set Statistics All the input values shwn in the Statistics has nt been btained frm viewers but instead have manipulated inside the flash file. In the future databases shuld be used fr string the infrmatin f persnalized graphs. The gal it s shw that it is pssible t cmpare inputs which hpefully will give a greater understanding f hw they are related t each ther. It is mrever pssible t zm in int the graph by viewing statistics day by day. The user can chse a day f interests and an additinal graph pps up with statistics frm the chsen day. The user can als find infrmatin fr each hur. If the users find it interesting they can chse the hur inside the day graph by stepping t the preferred hur. A new infrmatin bx appears with infrmatin prvided at that hur. Since the viewer are sitting in frnt f the televisin with nly the remte cntrl t navigate thrugh ut the flash applicatins, it is imprtant t discuss hw much infrmatin the viewers shuld have access t. Sme infrmatin might nt be suitable t shw when the viewers are in TV mde i.e. sitting relaxed in the cuch, instead prvide it separately n a cmputer. It can be discussed if the viewers shuld have access t this infrmatin at all. Maybe if they have a private site n the web cnnected t the system where they can mdify their 38

39 persnal infrmatin and access all infrmatin there. Due t this, I have separate Statistics and Camera frm the ther menus. At this mment the graph shws a gathering f all inputs value n a daily time line, i.e. the day and time the infrmatin was prvided are taken in t cunt but nt the week, mnth, and year. An additinal wrk wuld be implement the graph t makes it pssible t chse year, mnth, and week f interests Recmmendatins The purpse f understanding which inputs are available is in the end t generate recmmendatins based n thse. The aim f this prject was nt t generate recmmendatins but t shw hw thse inputs culd be used t give the user prfile infrmatin abut the viewer. The purpse f this menu is t shw that it culd be pssible t chse inputs which the recmmendatins shuld be based. It shuld als be pssible t get recmmendatin withut prviding any actins. Since it might be difficult t give the different implicit inputs a prper rating value, using the Multidimensinal recmmendatin mdel (see sectin 4.5.3) might be the answer. This mdel makes it pssible t get recmmendatins nt nly the crrelatin user item but als take the cntext as input. 7.7 Evaluatins In the future a full scale test f the flash dem is necessary. By full scale test I mean integrate the flash prject in a set-tp-bx and prvide it t a test grup in rder t understand hw the system wrks. The test shuld be dne by studying participants when they use the finally implemented prttype which wrks alngside the televisin. Observing when the viewers watch TV by using vide cameras and micrphnes t see hw it acts in natural envirnments. And extend the knwledge abut the system by asking questins abut the viewers feelings, thughts, and hw they experience the prject. This infrmatin is needed in rder t iterate the design prcess further t increase the usability. It is als imprtant in rder t get everything t wrk t create a full wrking database in which infrmatin abut the user will be stred. This will make it easier t gather all infrmatin needed which afterwards culd be cllected and analyzed. This infrmatin is als crucial t use when the system predict recmmendatins. Since the average husehld d nt have living rm installed with the newest bject recgnitin table and TV-sets with a build in camera, thse inputs are extremely hard t test in a natural envirnment. Therefre, I recmmend ding tests in a test envirnment because if will shw if it is any idea t keep thse inputs in future systems. The viewer might give the impressin f nt understanding them r the inputs d nt prvide the amunt f infrmatin needed. It is mrever imprtant t let the participants analyze hw well the recmmendatins fit the viewers. This infrmatin will lead t a better 39

40 understanding abut what implicit inputs prvide mre certain data and which d nt. Since users are different and like different things, the same inputs will nt suite all viewers. Fr example fr thse viewers which d nt have a favurite place in the TV-rm d nt benefit f that infrmatin. 7.8 Lgin functin At this pint there is n lgin functin in the flash prject which means that the system des nt knw wh is watching. A lgin functin culd easily be added in the set-tp-bx where the viewer culd explicitly create a lgin-prfile fr each family member whm they culd access by using the remte cntrl. In this case the system then knws the number f peple using the system, their age, gender and s frth. Other ways f accessing the persnal prfile withut interacting with the remte cntrl culd be using bimetrics such as vice detectin, face recgnitin, fingerprint reader, palm reader, r ther unique bdy features. One advantage by using bimetrics is the viewers always have their bdy with them and therefre have the pprtunity t lgin int systems lcated in ther hmes. Fr example, if yu visit a friend which uses the same set-tp-bx, yu use the lgin methd t access yur persnal prfile. The bimetrics preferred lgin methd in this system wuld be vice recgnitin since a micrphne already integrated t the system. Of curse a lt f further effrt has t be dne in rder t make it wrk. The infrmatin, e.g. actins and behaviurs, stred by the system when viewers are lgged in can als be used when the viewer are nt lgged in. By cmparing this infrmatin with thse gathered when the viewer are nt lgged in can then be used in rder t guess wh the viewer is. Fr example, if the viewers have nt lgged in but have the same mvements as Je De, the system guesses the nt lgged in viewer is Je De. The system culd prvide the viewer with recmmendatins which then are linked t his prfile. A prblem with this is if the viewer is nt Je De and the system prvides the unidentified viewer with Je De s prfile. Hw will Je De feel abut this and the unidentified viewer might be annyed since the recmmendatins and infrmatin are nt applied frm him. An additinal functinality by nt using lg in functin is using all infrmatin gathered and crss reference it with infrmatin already prvide by MMS t guess what stere type the viewer is. Maybe integrate a guess wh I am buttn which the viewer can push and the system gives there guess wh the persn is, in the matter f age, gender, ccupatin, interests, and ther persnalize. 7.9 Additinal functinalities It might be a gd idea t mix implicit inputs with explicit inputs whenever it is pssible. The mix f bth inputs culd help the system t better understand the viewer s behaviur and actins. Fr example using a rating scale as an explicit input, and when a mvie are being high rated, use all ther infrmatin gathered during the mvie as measurements fr a gd mvie. This culd give an understanding f hw implicit inputs shuld be interpreted. Fr instance when the user gives a mvie gd rates, she always pause when leaving the rm and the level f mtins and sund are lw but when mvie gets lw rate, the mtin and 40

41 sund levels are high. This infrmatin culd then be used t give implicit rates fr mvies. It might even be useful t smehw let the viewer rate the recmmendatin. This infrmatin culd be used as measurements t weight parameters in the algrithm in the hpe f prducing better fitted recmmendatins Prblems and slutins T make it easy t understand and wrk with implicit inputs it is highly imprtant t use a remte cntrl with high usability. If the system uses remte cntrl as an input it shuld be easy t access all features therwise the features that have an easier access wuld be used mre ften by the viewer. This might nt have anything t d with their actual wishes but mre abut their laziness. Fr example the system interpret accessing shws by using the EPG-functin the remte better then accessing them by pressing the right number there shuld be n difference in the level f accessing them. T be able t give the right infrmatin, the remte cntrl shuld have easy access t bth f the buttns. And with an easy navigatin it will als reduce the errr actins made by the viewer. It wuld then reduce the errr in the input infrmatin and the system des nt need t take this errr int accunt. A well designed remte cntrl with high usability is imprtant in the case f using a lgin functin. Because if the lgin prcedure is t cmplex and takes t much effrt as discussed abve, peple will nt use it unless it is necessary, r it prvides the user with a reasnable amunt f credit. 41

42 42

43 References 1. Watching televisin ver an IP netwrk. Meeyung Cha, Pabl Rdriguez, Jn Crwfrft, Sue Mn, Xavier Amatriain. Vuliagmeni, Greece : Internet Measurement Cnference, Prceedings f the 8th ACM SIGCOMM cnference n Internet measurements. 2. Peters, Jean-Jacques. Televisin 50 years. s.l. : Eurpean Bradcasting Unin (EBU), Teracm. Histrisk övergång till digital-tv. s.l. : Teracm.cm, ; 16: Lundgren, Anders. HDTV. PpVet Ppulärvetenskap ; 16: Chediak, Mark. As DVD Sales Fas-Frward, Retails Reduce VHS Stck. Washingtn Pst Media gallery TV: vew and shp yur phts n interactive digital televisin. Sabine Thieme, Ansgar Scherp, Melanie Albrecht, Susanne Bll. Hiltn, Singapre : Internatinal Multimedia cnference, Prceedings f the 14th annual ACM interantinal cnference n Multimedia. 7. A user prfile-based Persnlizatinsystem fr digital multimedia cntent. Diana Wiess, Jhannes Sheuerer, Michael Wenleder. Athen, Greece : ACM Internatinal Cnference Prceeding series, Prceedings f the 3rd internatinal cnference n Digital Interactive media in entertainment and arts. Vl New Insights n Internet Streaming and IPTV. Zhen Xia, Fan Ye. Niagra Falls, Canada : Cnference n Image and Vide Retrieval, Prceedings n the 2008 internatinal cnference n Cntent-based image and vide Retrieval. 9. Grid Cmputing Fcus Grup. Distributed Vide-n-Demand - A grid based VD slutin. s.l. : Infsys Technlgies Limited, Jbling, Hug. Sny Bravia WE5 Debuts. TrustedReviews. Debuts/p1, ; 09: MMS. Årsrapprt s.l. : MMS, ; 12: MMS ; kl: 10: Recmmender systems. Paul Resnick, Hal R. Varian. 3, s.l. : Cmmunicatins f the ACM, 1997, Vl The rle f transparency in recmmender systems. Rashmi Sinha, Kirsten Swearingen. Minneaplis, USA : Cnference n Uman Factrs in Cmputing 43

44 Systems, CHI'02 extended abstracts n Human factrs in cmputing systems. 15. C^2: A Cllabrative Recmmendatin System Based n Mdal Symblic User Prfile. Byrn Leite, Dantas Bezerra, Francisc de Assis, T. Carvalh, Valmir Macari. s.l. : Prceedings f the 2006 IEEE/WIC/ACM Internatinal Cnference n Web Intelligence table f cntents, Web Intelligence. 16. Mukesh Nathan, Chris Harrisn, Svetlanda Yarsh, Lren Terveen, Larry Stead, Brian Ament. CllabraTV: Making televisin viewing scial again. ACM Internatinal Cnference Prceeding Series. 2008, Vl Discvering User Prfiles. Riddhiman Chsh, Mhamed Dekhil. Madrid, Spain : Internatinal Wrd Wide Web cnference, Prceedings f the 18th internatinal cnference n Wrld Wide Web. 18. Merriam-Webster. s.l. : Merriam-Webster Implicit interest indicatrs. Mark Clavpl, Phng Le, Makt Wased, David Brwn. Santa Fe, New Mexic, USA : Internatinal Cnference f Intelligent User Interfaces, Prceedings f the 6th internatinal cnference f Intelligent user interfaces. 20. amazn.cm. s.l. : amazn.cm ; 13: Knwing the user's every mve: User activity tracking fr web site suability evaluatin and implicit interactins. Richard Atterer, Mnika Wnuk, Albrecht Schmidt. Edinbergh, Sctland : Interatinal Wrld Wide Web Cnference, Prceedings f the 15th internatinal cnference n Wrld Wide Web table f cntents. 22. Implicit human cmputer interactin thrugh cntext. Shmidt, Albrecht. 2, s.l. : Persnal and Cmputing, 2000, Vl Implicit Rating and Filtering. Niclas, David M. Budapest, Hungary : ARIADNE Prjects n Digital Libraries, Prceedings f the 5th DELOS Wrkshp n Filtering and Cllabrative Filtering. 24. Twards implicit interactin by using wearable interactin device sensrs fr mre than ne task. Hendrik Witt, Hlger Kenn. 20, Bankk, Thailand : ACM internatinal cnference prceedings, 2006, Vl TiV: Making shw recmmendatins using a distributed cllabrative filtering architecture. Kamal Ali, Wijanad van Stam. Seattle, USA : Internatinal Cnference n Knwledge Discvery and data mining, Prceedings f the tenth ACM SIGKDD internatinal cnference n Knwlage discvery and data mining. 26. Addressing uncertainty in implicit preference. Sandra Clara Gadanh, Niclas Lhuillier. Minneaplis, USA : ACM Cnference f Recmmender 44

45 systems, Prceedings f the 2007 ACM Cnference n Recmmender Systems. 27. Incrprating cntextual infrmatin in recmmender systems using a multidimensinal apprach. Gediminas Admavicius, Rames h Sankaranarayanan, Shahana Sen, Alexander Tuzhilin. 1, s.l. : ACM Transactins n Infrmatin Systems (TOIS), 2005, Vl Interactive televisin: New genres, new frmat, new cntent. Jensen, Jens F. Sydney, Australia : ACM Internatinal Cnference Prceeding Series, Prceedings f the secnd Australasian cnference n Interactive entertainment. Vl The Televisin Will Be Revlutinized: Effects f PVRs and Files having n Televisin Watching. Barry Brwn, Luise Barkhuus. Mnteral, Canada : Cnference f Human Factrs in Cmputing Systems, Prceedings f the SIGCHI cnference n Human Factrs in cmputing systems. 30. MMS - Mediamätning i Skandinavien. MMS statusrapprt m PVR - hårddiskapparater för tv-tittande i Sverige. Stckhlm, Sweden : MMS, ; 11: LRG - Leichtman Research Grup. DVRs nw in ver ne-quarter f US husehlds. Durham, USA : LRG, Jhn Wiley and Sns, ltd. Interactin design - beynd human-cmputer interactin. Barcelna, Spain : s.n., Vl. 2nd. 33. Eugen C. Nelsn, Paul B Batalden, Marjrie M. Gdfrey. Quality by design: A clinical Micrsystems apprach. San Franciscn : Jssey-Bass, adbe. s.l. : adbe.cm ; 14: Phidgets Inc. s.l. : Phidgets Inc ; 23:

46 46

47 Appendix A. Appendix A Survey: Answers and questins: 1. Vad är du? Man 32 71% Kvinna 13 29% 2. Hur gammal är du? <10 0 0% % % % % % % % 47

48 % % 80< 0 0% 3. Hur fta ser du på film? Välj nedan...00% Varje dag 1 2% Några gånger i veckan 17 38% En gånger i veckan 11 24% Några gånger i månaden 12 27% En gång i månaden 4 9% Någn gång per halvår 0 0% Någn gång per år 0 0% 4. Hur fta ser du på TV? Välj nedan... Varje dag 20 44% Några gånger i veckan 18 40% En gånger i veckan 2 4% Några gånger i månaden 1 2% En gång i månaden 1 2% Någn gång per halvår 1 2% Någn gång per år 2 4% 48

49 5. Hur fta ser du på filmer sm du redan har sett? (Om aldrig, gå till fråga 7) 1 Aldrig 3 7% % % % 5 Alltid 0 0% 6. Du tittar på samma film flera gånger, eftersm 6a. Du tycker filmen är bra 1 - Stämmer inte 0 0% 2 0 0% 3 2 5% % 5 - Stämmer helt 16 38% 6b. Du hade glömt brt att du har sett filmen förut 1 - Stämmer inte 19 45% % 3 3 7% 4 3 7% 49

50 5 - Stämmer helt 0 0% 6c. Filmen ger dig en bra "feeling" 1 - Stämmer inte 2 5% % % % 5 - Stämmer helt 5 12% 6d. Det är traditin (ex vid julen se Hw The Grinch Stle Christmas ) 1 - Stämmer inte 10 24% % % 4 3 7% 5 - Stämmer helt 5 12% 6e. Du ser den tillsammans med dina vänner sm inte har sett den förut ch gärna vill se den 1 - Stämmer inte 0 0% 50

51 2 7 17% % % 5 - Stämmer helt 5 12% 6f. Den råkar gå på TV ch det finns inget annat att se på 1 - Stämmer inte 8 19% 2 2 5% % % 5 - Stämmer helt 2 5% 6g. Någn annan rsak? 7. Hur fta ser du på extramaterialet? (Om aldrig, gå till fråga 9) 1 Aldrig 19 43% % % % 5 Alltid 0 0% 8. Efter att du har sett filmen tittar du på extramaterialet 8a. Du är intresserad av bakgrundsinfrmatin (ex hur säregna scener gjrdes ch andra detaljer) 51

52 1 - Stämmer inte 2 7% % % % 5 - Stämmer helt 3 11% 8b. Du gillade filmen mycket ch kan inte riktigt släppa den än 1 - Stämmer inte 2 8% % % % 5 - Stämmer helt 5 19% 8c. Du gillar se när flk gör brt sig ch passar på att se på "blppers" från filmen 1 - Stämmer inte 4 15% % 3 2 8% % 52

53 5 - Stämmer helt 5 19% 8d. Du vill veta mer m hur regissören tänkte (directrs ntes) 1 - Stämmer inte 6 23% % % % 5 - Stämmer helt 2 8% 8e. Du vill se brtklippta scener 1 - Stämmer inte 0 0% % % % 5 - Stämmer helt 4 15% 8f. Du har inget annat att göra 53

54 1 - Stämmer inte 8 31% % % 4 2 8% 5 - Stämmer helt 1 4% 8g. Någn annan rsak? Det kan finnas någt bra så varför inte se efter? man får en bättre helhetsbild av filmen..regissören berättar vad han menar med vissa scener sm man kanske inte tänkte på när man såg filmen sv. 9. Hur fta sätter du filmen på pause när du riktar uppmärksamheten på någt annat? (Om adrig, gå till fråga 11) 1 Aldrig 0 0% % % % 5 Alltid 5 11% 10. Filmen sätts på pause, du försvinner från vardagsrummet några minuter, när du kmmer tillbaka sätts filmen åter igen på play, eftersm 10a. Du vill inte missa det sm händer 54

55 1 - Stämmer inte 0 0% 2 0 0% 3 1 2% % 5 - Stämmer helt 27 61% 10b. Du gör det per autmatik, det spelar ingen rll m du gillar filmen eller inte 1 - Stämmer inte 10 23% % % % 5 - Stämmer helt 5 11% 10c. Du vill inte störa de andra genm att exempelvis springa runt i rummet eller skramla i köket 1 - Stämmer inte 8 18% % % % 5 - Stämmer helt 3 7% 10d. Någn annan rsak? 11. Hur fta använder du "rewind"-knappen? (Om aldrig, gå till fråga 13) 55

56 1 Aldrig 12 27% % % 4 3 7% 5 Alltid 0 0% 12. Du splar tillbaka för att se samma scen igen, eftersm 12a. Du missade någt sm hände eller sades 1 - Stämmer inte 1 3% % 3 3 9% % 5 - Stämmer helt 14 44% 12b. Du gillar den typen av händelse sm just utspelade sig (fighting, kärlek, musikaldansnummer, racing, etc) 1 - Stämmer inte 16 50% % 56

57 3 0 0% % 5 - Stämmer helt 2 6% 12c. Bakgrundsrelaterat; fina vyer eller någt syns i bakgrunden sm fångar ditt intresse 1 - Stämmer inte 10 31% % % % 5 - Stämmer helt 2 6% 12d. Du känner igen platsen sm scenen utspelar sig i 1 - Stämmer inte 13 41% % % % 5 - Stämmer helt 2 6% 12e. Någn annan rsak? En viktig händelse utspelar sig sm jag inte riktigt förstd första gången. 13. Du ser en film men efter en stund stänger du av filmen, eftersm 13a. Du tycker inte filmen var bra 57

58 1 - Stämmer inte 1 2% % % % 5 - Stämmer helt 17 38% 13b. Du hade redan sett filmen 1 - Stämmer inte 9 20% % % % 5 - Stämmer helt 6 13% 13c. Det var inte den filmen du hade tänkt se 1 - Stämmer inte 11 24% % % 58

59 4 5 11% 5 - Stämmer helt 4 9% 13d. Du fick någt annat att göra 1 - Stämmer inte 2 4% % % % 5 - Stämmer helt 9 20% 13e. Någn annan rsak? För trött för att se klart 14. Hur fta följer du två eller fler TV-prgram samtidigt? (Om aldrig, gå till fråga 16) 1 Aldrig 15 34% % % % 5 Alltid 5 11% 15. Du följer två eller fler TV-prgram samtidigt på lika kanaler, eftersm 15a. Du tycker att prgrammen är bra ch vill se dem 59

60 1 - Stämmer inte 2 7% 2 2 7% % % 5 - Stämmer helt 11 38% 15b. Inget av prgrammen tycker du är särskilt bra men de är det bästa sm visas just nu 1 - Stämmer inte 11 38% % % % 5 - Stämmer helt 0 0% 15c. Någn annan rsak? kan vara för att ha någt att se när det är reklam på den andra kanalen växlar i reklamen 16. Om du ska ta dig till en kanal sm du vet visar någt du vill se, hur gör du då? 16a. Använder EPG? (Elektrnisk prgram guide) 60

61 1 Aldrig 36 80% 2 2 4% 3 4 9% 4 3 7% 5 Alltid 0 0% 16b. Zappar dit? (använder upp/ner alt. +/-) 1 Aldrig 2 4% 2 3 7% % % 5 Alltid 11 24% 16c. Trycker in rätt nummer? 1 Aldrig 2 4% 2 3 7% % % 5 Alltid 8 18% 16d. Annat sätt? ser inte på TV Bläddrar mellan två kanaler med hjälp av R (senaste kanalen man kllade på) 17. Om du sätter dig framför TV:n ch vill veta vad sm visas just nu. Hur gör du då? 17a. Använder EPG? (Elektnisk prgram guide) 61

62 1 Aldrig 32 73% 2 1 2% 3 3 7% % 5 Alltid 1 2% 17b. Zappar? (använder upp/ner alt. +/-) 1 Aldrig 0 0% % % % 5 Alltid 7 16% 17c. Tittar på TextTV? 1 Aldrig 16 36% % % 62

63 4 7 16% 5 Alltid 4 9% 17d. Tittar i TV-tablån i tidningen? 1 Aldrig 24 53% % % 4 0 0% 5 Alltid 1 2% 17e. Internet? 1 Aldrig 1 2% 2 4 9% % % 5 Alltid 7 16% 17f. Frågar en vän? 63