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Personalizing Web Page Recommendations with Collaborative Filtering and Topic-Aware Markov Model

This paper presents a novel framework for personalized web page recommendations combining collaborative filtering and a topic-aware Markov model. The exponential growth of web pages complicates finding desired information, leading to aimless navigation. Our solution, PIGEON, emphasizes personalization and topical coherence, effectively tailoring recommendations to individual user interests and current tasks. The experimental evaluation demonstrates improved user satisfaction and engagement, leveraging user similarities and a graph-based clustering approach to enhance relevance in recommendations.

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Personalizing Web Page Recommendations with Collaborative Filtering and Topic-Aware Markov Model

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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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