Department of Computer Science

Machine Learning Research Group

University of Texas at Austin Artificial Intelligence Lab

Publications: 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.

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  1. Review Quality Aware Collaborative Filtering
    [Details] [PDF]
    Sindhu Raghavan and Suriya Ganasekar and Joydeep Ghosh
    In Sixth ACM Conference on Recommender Systems (RecSys 2012), 123--130, September 2012.
  2. Explaining Recommendations: Satisfaction vs. Promotion
    [Details] [PDF]
    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, January 2005.
  3. Explanation for Recommender Systems: Satisfaction vs. Promotion
    [Details] [PDF]
    Mustafa Bilgic
    Austin, TX, May 2004. Undergraduate Honor Thesis, Department of Computer Sciences, University of Texas at Austin.
  4. Content-Boosted Collaborative Filtering for Improved Recommendations
    [Details] [PDF]
    Prem Melville, Raymond J. Mooney, and Ramadass Nagarajan
    In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-02), 187-192, Edmonton, Alberta, 2002.
  5. Content-Boosted Collaborative Filtering
    [Details] [PDF]
    Prem Melville, Raymond J. Mooney, and Ramadass Nagarajan
    In Proceedings of the SIGIR-2001 Workshop on Recommender Systems, New Orleans, LA, September 2001.
  6. Content-Based Book Recommending Using Learning for Text Categorization
    [Details] [PDF]
    Raymond J. Mooney and Loriene Roy
    In Proceedings of the Fifth ACM Conference on Digital Libraries, 195-204, San Antonio, TX, June 2000.
  7. Content-Based Book Recommending Using Learning for Text Categorization
    [Details] [PDF]
    Raymond J. Mooney and Loriene Roy
    In Proceedings of the SIGIR-99 Workshop on Recommender Systems: Algorithms and Evaluation, Berkeley, CA, August 1999.
  8. Book Recommending Using Text Categorization with Extracted Information
    [Details] [PDF]
    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, 70-74, Madison, WI, 1998.
  9. Text Categorization Through Probabilistic Learning: Applications to Recommender Systems
    [Details] [PDF]
    Paul N. Bennett
    1998. Honors thesis, Department of Computer Sciences, The University of Texas at Austin.