Recommending TV programs on the Web Between content based retrieval and social filtering

1. GMD IPSI Recommending TV programson the WebBetween content based retrievaland social filtering Patrick BaudischGMD-IPSIMarch 3rd 98 March 8th 98 1

2. Credits • Thanks for the award (its almost done...)

3. Contents • Part 1 • About the project • Requirements • An evolving system • Personalization • Part 2 • Recommendation and cooperative aspects • Feedback & Conclusions

4. About the project • A cooperation between GMD-IPSI & • GMD: German national reserach institute for information technology • TV-TODAY • German printed TV program guide • They sell 1,400,000 copies per two weeks • Where are printed guides going when digital TV and video on demand emerge?

5. About the project • Goal: Help users in creating their personal TV schedule • None of the German Web-based TV program system gives more recommendation than their printed counterpart • Be more than a prototype, reach thousands of users • Learn German now!

6. Design criteria TV programs vs. books and movies • TV programs are a stream rather than a database=> We do not have much time to collect data for recommendations • TV programs are experienced as having a lower value => Require only low user effort • Users have experiences and therefore expectations from printed TV program guides (e.g. TV-TODAY)=> Start with what users expect

7. Design criteria • System must be easy to learn (WWW) => Do what people expect • Be spectacular (TV-TODAY) => Do what people don´t expect

8. 1 Familiarity Behave like printed TV program guides Retrieval Query/Browsing 2 guests members 3 Personalization & Filtering Adjust permanent settingsProfile, Push service An evolving system

9. First time user interface (guest mode)

10. 1 What’ on tonight • We ran user tests: 40% of first time users plan only for today • Press Start

11. Table cells color-coded List items have colored field Hue = Genre e.g. {sports=green, movie=red, ...} Genre Visualization

12. Recommendation Visualization • Color intensity = relevance • the darker the more recommended • less recommended programs fade to background color • What means “recommended”? (later slide)

13. 2 Retrieval: Adjust four parameters • Date interval • Time interval • Channels (predefined set) • Genre • Press Start

14. Genre hierarchy • A Genre is the set of programs that match a descriptor • Deeper genres are more specific • Guides users • Less universal than boolean search

15. -- Create account / login • To personalize users need an account • Store user data on server side • Use this data • matching users • making recommendations

16. Member user interface

17. 3 Personalization • Personalize three of four parameters • favorite times • favorite channels • favorite genre • (There are nofavorite dates)

18. Personalize times • Click yellow buttons into hour fields • Draw whole rectangles at once (Mac Paint)

19. Personalize channels

20. (Applet Demo) • Select the German regional stations that you can receive

21. Personalize genres • Check favorite genres • Use folder with favorite genres like bookmarks • Click “all favorite genres” to load all at once 8

22. Personal schedule (“grocery list”) • Select programs, print it out, take it home

23. Recommendation • How the colors are generated?

24. Is Social filtering applicable? (Diagram by Joe Konstan)

25. Applicability of the Ringo approach • Correlate users by the programs in the grocery list? • “In/Not in” info from the grocery list is much less informative than 7 ratings scale=> results of correlating people is rather poor • We don´t have unlimited time, only one week. • The database is not stable • User A just returned from a 2 week vacation • User B is a newbee • Correlate on a standard set of items means extra effort (amazon recommendation center) correlation?

26. Applicability of the Grouplens approach • GroupLens: Press 1,2, ..,7 to rate and go to the next article • Joseph A. Konstan says: These ratings require high cognitive costs • => Rating effort might be too much

27. D C B A Four types of recommendation Recommendationsby TV-TODAY “Size of the audience” Personal genre profile Opinion leaders

28. A Recommendations by TV-TODAY • The editors of TV-TODAY provide ratings for all movies of the day (60 of 1000 programs) • Ratings , , , • You agree or you don`t

29. B Size of the audience • Use programs in “Grocery list” as a recommendation for other users • We count how often a program occurs in users “grocery lists” • The more the better the rating • Works for all programs not only movies • Will lead your attention to events like“Tour de France” • Not personalized => might not fit your personal interests

30. Initialization of “Size of the audience” • Everyday one day is added, one removed • When a program is inserted into the system it is not in anyone´s “grocery list” • => Initialize ratings from the genre or series • Remove initialization during the week and replace with the real recommendations • “Grocery list” means: “I want to see that”. It does not mean “I like that” (how could I know before I`ve seen it) (and afterwards nobody cares) • Anyway: It works!

31. C Profile Personal genre profile • Describe favorite genres in more detail • Based on public recommendation, but users define offsets to adapt ratings to personal needs • Andrea´s personal TV interests • She is interested in sports, especially in basketball, where she does not want to miss a single program. • She wants to be up-to-date about current information without spending too much time on it. • Finally, for recreation, she wants to include some good action movies.

32. Form based Interface • Define how many programs of this genre to get • Define how personally important these are

33. Form based Interface • Define for all favorite genres • Initialization:Small is important(Law of Zipf)

34. Graphical user interface • Grey = cropped • Yellow = selected • Red = important

35. Drag boxes around • Box sizes reflect number of programs available per week

36. Evaluation: Number of subjects • (We just got started, ...) • Form based interface: • Graphical Profile Editor: 10 subjects

37. Comparison of the two interfaces • The graphical interface is much more difficult to learn than the form-based interface • The graphical interface provides more utility and is easier to use than the form-based interface • precision • graphical overview • => Provide a form-based interface for first-time users and a graphical interface for frequent users • Learnability: There seems to be a lack of methaphors (Where is Don Gentner?)

38. D Opinion leaders • Allow more individual users to generate recommendation (not only TV-TODAYs editors) • Loren/Phoaks: Not everybody wants to give recommendation, but some do • Take “Grocery lists” of an individual user as your personal source for recommendation(instead of summing all up)

39. Opinion leaders • Opinion leaders are represented as a folder containing their “grocery list” • An opinion leader behaves exactly like a genre • Users can have their favorite opinion leaders

40. Who benefits...? • Being an opinion leader means no extra work • “Don´t you want to become an opinion leader?” • But: Opinion leaders loose part of their privacy • Let´s reward them for that: • Give them program data one week in advance=> that helps initializing “size of the audience” • A free subscription to the printed guide • Tell them that it is “cool” to be one • Survival of the fittest: If a new opinion leader applies drop the one with the fewest subscribers

41. ... and who loses? • TV-TODAY editors can be opinion leaders • TV-TODAY didn´t like the idea too much :)

42. Evaluation of the overall system so far • Our group + TV-TODAY people (about 20 users) • Beta test at GMD IPSI with about 30 users • User tests with 10 users for 40 minutes each

43. Feedback • Orientation is easy, but undo is missing • For some users the system is still too complex (opening folders, buttons to small for elder users) • People liked the „grocery list“That´s good for our recommendation system • Overall it is useful and easy to use • High fun-factor! • „When will you go online?“

44. Future work • Go online! April 98 • Where else can we apply the described techniques: Usenet news, web pages, ... • Be more proactive: Push service, email notification of very important programs • Scott Robertson (digital libraries): Soft pushes

45. Future work: Cooperative stuff • We do have a “Find similar users” component(based on favorite genres and genre profiles) • Allow users to exchange their profiles • Become an opinion leader for individual users (friends, community) • Recommend genres and opinion leadersThis allows managing a greater number of them • Have specific opinion leaders • One that just recommends action movies, ... • Keep them inside the genre structure

46. Producers ofDocuments Users/Groups withLong-term goals Distributors ofDocuments RegularInformation Interest Distribution andRepresentation Representation DocumentSurrogates Comparisonor Filtering RetrievedDocuments Use and/orEvaluation Modification Profile creation is NOT JUST iterative • Three paths lead to Profiles 1. Creation Profiles 2. Outerrefinement cycle 3. Innerrefinement cycle

47. Conclusions • Traditionally a broadcast medium • TV makers broadcast, viewers watch • Editors write, readers read • Interactive and collaborative concepts are new here • The system contains a lot of functions, some of which are more complex • Users have months to discover all these functions • Until then retrieval is just fine • Many users will never push it that far • That´s ok!

48. The END • What do you think?