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1 G M DG M D I P S II P S I Recommending TV programs on the Web Between content based retrieval and social filtering Patrick Baudisch GMD-IPSI March 3.

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Presentation on theme: "1 G M DG M D I P S II P S I Recommending TV programs on the Web Between content based retrieval and social filtering Patrick Baudisch GMD-IPSI March 3."— Presentation transcript:


2 1 G M DG M D I P S II P S I Recommending TV programs on the Web Between content based retrieval and social filtering Patrick Baudisch GMD-IPSI March 3 rd 98 March 8 th 98

3 Credits Thanks for the award (its almost done...)

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

5 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?

6 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!

7 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

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

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

10 First time user interface (guest mode)

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

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

13 Recommendation Visualization Color intensity = relevance the darker the more recommended less recommended programs fade to background color What means recommended? (later slide)

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

15 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

16 Create account / login To personalize users need an account Store user data on server side Use this data matching users making recommendation s --

17 Member user interface

18 Personalization Personalize three of four parameters favorite times favorite channels favorite genre (There are no favorite dates) 3

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

20 Personalize channels

21 (Applet Demo) Select the German regional stations that you can receive

22 Personalize genres Check favorite genres Use folder with favorite genres like bookmarks Click all favorite genres to load all at once 8

23 Personal schedule (grocery list) Select programs, print it out, take it home

24 Recommendation How the colors are generated?

25 Is Social filtering applicable? (Diagram by Joe Konstan)

26 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?

27 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

28 Four types of recommendation A Recommendations by TV-TODAY Size of the audience Personal genre profile Opinion leaders B C D

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

30 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 B

31 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!

32 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. C Profile

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

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

35 Graphical user interface Grey = cropped Yellow = selected Red = important

36 Drag boxes around Box sizes reflect number of program s available per week

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

38 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?)

39 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) D

40 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

41 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

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

43 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

44 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?

45 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

46 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 leaders This allows managing a greater number of them Have specific opinion leaders One that just recommends action movies,... Keep them inside the genre structure

47 Profile creation is NOT JUST iterative Three paths lead to Profiles 1. Creation 2. Outer refinement cycle 3. Inner refinement cycle Producers of Documents Distributors of Documents Distribution and Representation Document Surrogates Regular Information Interest Users/Groups with Long-term goals Representation Comparison or Filtering Modification Use and/or Evaluation Retrieved Documents Profiles

48 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!

49 The END What do you think?

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