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Motivation Recommender framework Experimental evaluation Conclusions

Personalizing Web Page Recommendation via Collaborative Filtering and Topic-Aware Markov Model. Qingyan Yang, Ju Fan, Jianyong Wang, Lizhu Zhou Database Research Group, DCS&T, Tsinghua University. Agenda. Motivation Recommender framework Experimental evaluation Conclusions. Motivation

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Motivation Recommender framework Experimental evaluation Conclusions

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  1. Personalizing Web Page Recommendation via Collaborative Filtering and Topic-Aware Markov Model Qingyan Yang, Ju Fan, Jianyong Wang, Lizhu ZhouDatabase Research Group, DCS&T, Tsinghua University

  2. Agenda Motivation Recommender framework Experimental evaluation Conclusions DB Group, DCS&T, Tsinghua University

  3. Motivation Recommender framework Experimental evaluation Conclusions DB Group, DCS&T, Tsinghua University

  4. Motivation • The Web is explosively growing • By the end of 2009 (source: the 25th Internet Report, 2010) • 33,600,000,000 Web pages in China • Twice as many as that in 2003 • Finding desired information is more difficult. • Users often wander aimless on the Web without visiting pages of his/her interests • Or spend a long time on finding the expected information. 8/6/2014 DB Group, DCS&T, Tsinghua University 4

  5. Web page recommendation DB Group, DCS&T, Tsinghua University

  6. Web page recommendation • Objective • To understand users' navigation behavior • To show some pages of users' interests at a specific time • Existing popular solutions • Markov model and its variants • Temporal relation is important. If the browsing sequence is "A B C … A B C … A B C", Then C is recommended when A and B are visited one after another 8/6/2014 DB Group, DCS&T, Tsinghua University 6

  7. Limitations • No personalized recommendations • All users receive the same results • Topic information of pages is neglected. • Two pages, which are sequentially visited, may be very different in terms of topics. DB Group, DCS&T, Tsinghua University

  8. PIGEON: our solution • Personalized Web page recommendation • Two novel features • Personalization • Meet preference of different users People? I am a blog about finance Stocks? Wikipedia? History? …… DB Group, DCS&T, Tsinghua University

  9. PIGEON: our solution • Two novel features • Personalization • Topical coherence • To be relevant to users' present missions Hotel Nearby scenic spots discount Airline …… DB Group, DCS&T, Tsinghua University

  10. Motivation Recommender framework Experimental evaluation Conclusions DB Group, DCS&T, Tsinghua University

  11. Recommender framework DB Group, DCS&T, Tsinghua University

  12. Data representation • Navigation graph Edge: jump relation A K 3 2 2 2 Weight: relation frequency B D G 4 6 2 2 L 2 1 Web page Jump relation H J E I 1 C 1 M F DB Group, DCS&T, Tsinghua University

  13. Topic discovery • Basic idea • We assume that pages with similar URLs or evolved in jump relations are topically relevant. • URLs Features • Keywords. e.g., http://dblp.uni-trier.de/db/index.html • Expanded by Manifold-based keyword propagation • Web page clustering • Each cluster represents one topic DB Group, DCS&T, Tsinghua University

  14. Example A K 3 2 2 2 B D G 4 6 2 2 L 2 1 E H I J 1 C 1 M F DB Group, DCS&T, Tsinghua University

  15. Topic-Aware Markov Model • Take n-grams as states. e.g., n=2 • Web page preference score • Maximum likelihood estimation • e.g., P(D|BC) = f(BCD)/f(BC) = 1/2 ABCD B C A ABCD B C A A C C A, B D B AB BC CD DB CA AB BC CD AC CC CA DB CA BD DB Temporal state Topical state DB Group, DCS&T, Tsinghua University

  16. Personalized Recommender • Collaborative filtering • Basic idea • Web page preference • user • similarities DB Group, DCS&T, Tsinghua University

  17. User Similarity • User profile • A set of topics • Similarity measurement • Topic similarity • Maximum weight matching 0.9 1.0 0.8 DB Group, DCS&T, Tsinghua University

  18. Motivation Recommender framework Experimental evaluation Conclusions DB Group, DCS&T, Tsinghua University

  19. Experiment settings • Data set • 1,402,371 records of 375 users in 34 days • First 30 days for training and 4 days for testing • Metrics are precision and recall • Comparative methods DB Group, DCS&T, Tsinghua University

  20. Experimental evaluation 1st-order model 2nd-order model DB Group, DCS&T, Tsinghua University

  21. Motivation Recommender framework Experimental evaluation Conclusions DB Group, DCS&T, Tsinghua University

  22. Conclusions • Taking user similarities into account, we could recommend Web pages to meet different users' preferences. • We discover users' interested topics using an effective graph-based clustering algorithm. • We devise a topic-aware Markov model to learn navigation patterns which contribute to the topically coherent recommendations. DB Group, DCS&T, Tsinghua University

  23. THANKS DB Group, DCS&T, Tsinghua University

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