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CSE 6362.003 Intelligent Environments Paper Presentation Darin Brezeale April 16, 2003

CSE 6362.003 Intelligent Environments Paper Presentation Darin Brezeale April 16, 2003. Surfing the Digital Wave. Generalizing Personalized TV Listings using Collaborative, Case-Based Recommendation Barry Smyth, Paul Cotter Dept. of Computer Science University College Dublin.

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CSE 6362.003 Intelligent Environments Paper Presentation Darin Brezeale April 16, 2003

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  1. CSE 6362.003 Intelligent Environments Paper Presentation Darin Brezeale April 16, 2003

  2. Surfing the Digital Wave Generalizing Personalized TV Listings using Collaborative, Case-Based Recommendation Barry Smyth, Paul Cotter Dept. of Computer Science University College Dublin

  3. Paper Source • Published: In proceedings of the third International Conference on Case-based Reasoning. Munich,  Germany, 1999. • URL: http://www.cs.ucd.ie/staff/bsmyth/home/crc/iccbr99a.ps

  4. Introduction • Cable and satellite services make it possible to have hundreds or thousands of television channels available • TV Guide is over 400 pages • Channel surfing 200 channels at 10 seconds each will take nearly 35 minutes

  5. Introduction cont. • Problem: It is difficult for viewers to locate television programs they may be interested in. • Solution: Create a system that will identify and recommend programs of interest to the viewers.

  6. PTV System • Paper describes the PTV system (Personalized Television Listings) • Online system http://www.ptv.ie/ (listed in paper as http://ptv.ucd.ie) • Registered users can view personalized TV listings

  7. Profile Database and Profiler Program Case-Base Schedule Database Recommender Guide Compiler PTV Architecture

  8. Profile Database and Profiler Stores profiles of each user, including: TV programs liked and disliked Preferred viewing times Subject preferences Preliminary profiles constructed at registration Helps to initiate the personalization process Most profile information learned from user grading of recommendations PTV Architecture cont.

  9. Program Case-Base Database of TV program content descriptions, including: Title Genre Cast PTV Architecture cont.

  10. Schedule Database Contains TV listings for all supported channels Constructed from online sources Recommender The brain of the PTV system Takes user profile information and selects new TV programs to recommend PTV Architecture cont.

  11. Guide Compiler Personalized listings are constructed dynamically by matching: List of recommended TV programs and the user’s likes TV programs to be aired on the specified date PTV Architecture cont.

  12. PTV Architecture cont.

  13. Hybrid Information Filter • PTV makes recommendations by combining two differrent approaches • Case-based • Collaborative Filtering

  14. Case-based Approach • Matches features in the user’s profile to TV programs Schema(u) = feature-based representation of u’s profile p = program case wi = weight of program feature i fi = program feature i

  15. Case-based Approach cont. • Pros • Based strictly on the user’s profile • Cons • Knowledge-engineering effort to develop case representations and similarity models • Recommendations will be very similar to previously viewed TV programs

  16. Collaborative Filtering Approach • Recommendations are based on what similar users like • k similar user profiles are selected using function PrfSim • r programs are selected for recommendation using function PrgRank

  17. Collaborative Filtering Approach cont. r(piu) = rank of program pi in profile u p(u) = ranked programs in user u’s profile

  18. Collaborative Filtering Approach cont. • Pros • No need for rich content representation • Increased recommendation diversity • Cons • Cost to gather enough profile information to make accurate similarity measures • Latency of new shows spreading

  19. Experimental Studies • Setup • About 200 users • Mainly students and staff from University College Dublin and Trinity College Dublin • Case-base consisted of about 400 TV programs • 2000 individual program guides were requested • Each guide contained an average of 3 recommendations

  20. Experimental Studies cont. • Method • Recommendations in each guide were either: • generated by the case-based approach • generated by the collaborative filtering approach • generated by picking programs at random • Users graded recommendations with values of {-2, -1, 0, 1, 2} • About 1000 individual gradings from 100 users

  21. Experimental Studies cont. • Results • Performance measured by counting percentage of users receiving ‘n’ or more good recommendations per day • Results shown in figure

  22. Experimental Studies cont.

  23. Conclusions • Case-based and collaboritive filtering approaches offset each other’s weaknesses • Collaborative filtering approach outperformed case-based approach • Both collaborative filtering and case-based approaches outperformed random recommendations

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