Learning for Recommender Systems
Recommender systems suggest information sources and products to users based on learning from examples of their likes and dislikes. Most existing recommender systems use collaborative filtering methods that base recommendations on other users' preferences. By contrast, content-based methods use information about an item itself to make suggestions. This approach has the advantage of being able to recommended previously unrated items to users with unique interests and to provide explanations for its recommendations. Our work has focused on a content-based book recommending system called LIBRA. We have also explored combining our content-based approach and standard collaborative filtering.
People
Shruti Bhosale Formerly affiliated Masters Student shruti [at] cs utexas edu
Aishwarya Padmakumar Ph.D. Alumni aish [at] cs utexas edu
Publications
Review Quality Aware Collaborative Filtering 2012
Sindhu Raghavan, Suriya Ganasekar, and Joydeep Ghosh, In Sixth ACM Conference on Recommender Systems (RecSys 2012), pp. 123--130, September 2012.
Explaining Recommendations: Satisfaction vs. Promotion 2005
Mustafa Bilgic and Raymond J. Mooney, In Proceedings of Beyond Personalization 2005: A Workshop on the Next Stage of Recommender Systems Research at the 2005 International Conference on Intelligent User Interfaces, San Diego, CA, J...
Explanation for Recommender Systems: Satisfaction vs. Promotion 2004
Mustafa Bilgic, unpublished. Undergraduate Honor Thesis, Department of Computer Sciences, University of Texas at Austin.
Content-Boosted Collaborative Filtering for Improved Recommendations 2002
Prem Melville, Raymond J. Mooney, and Ramadass Nagarajan, In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-02), pp. 187-192, Edmonton, Alberta 2002.
Content-Boosted Collaborative Filtering 2001
Prem Melville, Raymond J. Mooney, and Ramadass Nagarajan, In Proceedings of the SIGIR-2001 Workshop on Recommender Systems, New Orleans, LA, September 2001.
Content-Based Book Recommending Using Learning for Text Categorization 2000
Raymond J. Mooney and Loriene Roy, In Proceedings of the Fifth ACM Conference on Digital Libraries, pp. 195-204, San Antonio, TX, June 2000.
Content-Based Book Recommending Using Learning for Text Categorization 1999
Raymond J. Mooney and Loriene Roy, In Proceedings of the SIGIR-99 Workshop on Recommender Systems: Algorithms and Evaluation, Berkeley, CA, August 1999.
Book Recommending Using Text Categorization with Extracted Information 1998
Raymond J. Mooney, Paul N. Bennett, and Loriene Roy, In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98)"-REC-WKSHP98, year="1998, pp. 70-74, Madison, WI 1998.
Text Categorization Through Probabilistic Learning: Applications to Recommender Systems 1998
Paul N. Bennett, unpublished. Honors thesis, Department of Computer Sciences, The University of Texas at Austin.
Labs
Machine Learning