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TV Scout Lowering the entry barrier to personalized TV program recommendation

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  1. TV ScoutLowering the entry barrier topersonalized TV program recommendation Patrick Baudisch &Lars Brueckner AH 2002June 1th 2002

  2. Contents • Motivation • TV Scout user interface • Retrieval part… • …leading to the filtering part • Results of usage data analysis • Conclusions

  3. Motivation: Information overload • Too many research papers, books, movies, web pages… even TV programs • Germany: printed program guides list 10.000 programs per two weeks • Content of interest has not increased proportionally  planning TV has become a challenge* • Goal: Reduce the set of programs that users have to look at to find relevant programs Allow users to watch TV more selectively

  4. The initial concept… • We wanted to offer:personalized TV program listings“at a single mouse click” • Resulting user interaction:“Sure, we’ll tell you what’s on tonight, but before we do that, please answer these 30 questions…” • Guess how users liked that…

  5. We did some field work… • Users’ expectations are inspired by printed TV program guides • Step 1: Find the right listing • Step 2: Sift through the listing • Step 3: Remember or mark-up programs to watch • Step 4: Watch •  User interface design challenge: • Pick people up where they are (printed TV program guides) • … • …and guide them to personalized listings at a mouse click

  6. Exact match Step 1: Select a query Best match

  7. retentionmenus Step 2+3: Read & retain program descript. programdescription table programdescriptionlist

  8. retentionmenus laundry list video labels Step 4: Print it out & watch TV programdescription table programdescriptionlist

  9. Printed program guide Step 1: Pick the right listing Step 2: Sift through listing Step 3: Mark-up programs Step 4: Watch TV Scout Step 1: Pick the right query Step 2: Sift through listing Step 3: Retain programs Step 3b: Print it out Step 4: Watch Emulating a printed guide

  10. But then: suggestions and bookmarks

  11. Personalized schedules at a mouse click

  12. Not that users have to, but…

  13. T1 T2 T T3 system provides system suggests system compiles system learns start queries S1 bookmarkedqueries S2 one-clickTV program S3 user writes user defines user updates U1 U2 U3 Summary of usage

  14. TV Scout usage data • TV Scout user interface concept= delayed disclosure of the filtering functionality • Does this actually reduce the entry barrier to personalized filtering? • => Informal analysis of log file data from actual web usage

  15. Procedure • 18 months of log file data, extracted from the web server log files and the system’s database • Gathered data • 10,676 registered users • In total, users had executed 48,956 queries • 53% of all queries (25,736 queries) were specific queries different from the default query. • Bias: the suggestion feature became available later

  16. Goals • Goal 1: Repeated usage would indicate that users had taken the entry hurdle • Goal 2: Learn more about the users’ demand for the offered filtering functionality: How many would use bookmarking and/or query profiles? • Goal 3: How useful users would find the query profile. Query profile users, would they use or abandon it?

  17. Results & conclusions • Repeated log-ins:9,190 of 10,676 users logged in repeatedly (= 86%) • Very high percentage for a web-based system • => Delayed disclosure of filtering functionality is a successful approach to keeping the entry barrier for first-time users low

  18. Results & conclusions • Bookmarks & Query profiles • 1770 users had bookmarked 4383 queries (= 17%) • 270 users executed query profile (= 15% of bookmark users) • They executed their query profiles 5851 times (21 times per user). • Once they used the profile they liked it • Only 17% used filtering functionality, isn’t that low? • Survey: only 12% of the users of printed TV guides planned TV schedule for a week or longer • => The 83% non-bookmark users may have found retrieval to be the appropriate support for their information seeking strategy • Future work: An online survey as well as an experimental study should help to verify this interpretation.

  19. Thanks to: Dieter Böcker, Joe Konstan, Marcus Frühwein, Michael Brückner, Gerrit Voss, Andreas Brügelmann, Claudia Perlich, Tom Stölting, Diane Kelly, and TV TODAY Further reading & demo: END

  20. If time left • Explain system architecture • Demo paintable interfaces

  21. Date program descriptions feedback Time Profile Time Dialog Exact match filtering ChannelProfile ChannelDialog query QSA profile hoc QSA filtering ad Text search Genres Editors’ tips User tips Query subsystems Retention tools Laundrylist Videolabels Estim. Pop. ACF Content provider Program description database Movie database TV ScoutArchitecture

  22. Slides to bring up during questions

  23. QSA profile editor (experts) viewing time profile editor channelprofile editor QSAprofile editor suggest queries QSAmenu query menus textsearch retentionmenus programdescription table laundry list video labels programdescriptionlist TV Scout UI TV Scout interface with starting page

  24. userprofile QSAprofile q1 … qn e.g. news,sports, Comedyshows A How does user like news compared to sports…? Structure of TV Scout user profiles

  25. TV listing& table  retention tools TV Scout: retrieval usage summary Cooperation with German TV TODAY 17,000 registered users

  26. Further reading • P. Baudisch. Dynamic Information Filtering. Ph.D. Thesis. GMD Research Series 2001, No. 16. GMD Forschungszentrum Informationstechnik GmbH, Sankt Augustin. ISSN 1435-2699, ISBN 3-88457-399-3. • P. Baudisch. Recommending TV Programs on the Web: how far can we get at zero user effort? In Recommender Systems, Papers from the 1998 Workshop, Technical Report WS-98-08, pages 16-18, Madison, WI. Menlo Park, CA: AAAI Press, 1998. • P. Baudisch. The Profile Editor: designing a direct manipulative tool for assembling profiles. In Proceedings of Fifth DELOS Workshop on Filtering and Collaborative Filtering, pages 11-17, Budapest, November 1997. ERCIM Report ERCIM-98-W001. • P. Baudisch. Using a painting metaphor to rate large numbers of objects. In Ergonomics and User Interfaces, Proceeding of the HCI '99 Conference, pages 266-270, Munich, Germany, August 1999. Mahwah: NJ: Erlbaum, 1999.