UT ML Group: 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.

Publications

  1. Explaining Recommendations: Satisfaction vs. Promotion [Abstract] [PDF]
    Bilgic, M. and Mooney, R.J.
    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, January 2005.

  2. Explanation for Recommender Systems: Satisfaction vs. Promotion [Abstract] [PDF]
    Mustafa Bilgic
    Undergraduate Honor Thesis, Department of Computer Sciences, University of Texas at Austin, May 2004.

  3. Content-Boosted Collaborative Filtering for Improved Recommendations [Abstract] [PDF]
    Prem Melville, Raymond J. Mooney, and Ramadass Nagarajan
    Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-2002), pp. 187-192, Edmonton, Canada, July 2002.

  4. Content-Boosted Collaborative Filtering [Abstract] [PDF]
    Prem Melville, Raymond J. Mooney, and Ramadass Nagarajan
    Proceedings of the SIGIR-2001 Workshop on Recommender Systems, New Orleans, LA, September 2001.

  5. Content-Based Book Recommending Using Learning for Text Categorization [Abstract] [PDF]
    Raymond J. Mooney and Loriene Roy
    Proceedings of the Fifth ACM Conference on Digital Libraries, San Antonio, TX, pp. 195-204, June 2000.

  6. Content-Based Book Recommending Using Learning for Text Categorization [Abstract] [PDF]
    Raymond J. Mooney and Loriene Roy
    Proceedings of the SIGIR-99 Workshop on Recommender Systems: Algorithms and Evaluation, Berkeley, CA, August 1999.

  7. Book Recommending Using Text Categorization with Extracted Information [Abstract] [PDF]
    Raymond J. Mooney, Paul N. Bennett, and Loriene Roy
    The AAAI-98/ICML-98 Workshop on Learning for Text Categorization and the AAAI-98 Workshop on Recommender Systems, pp. 49-54, pp. 70-74, Madison, WI, July 1998.

  8. Text Categorization Through Probabilistic Learning: Applications to Recommender Systems [Abstract] [PDF]
    Paul N. Bennett
    Undergraduate Honor Thesis, Department of Computer Sciences, University of Texas at Austin, May 1998.
    Also appears as Technical Report AI 98-270, Artificail Intelligence Lab, University of Texas at Austin.
    29 pages

mooney@cs.utexas.edu