Leveraging aggregate ratings for better recommendations

ABSTRACT

The paper presents a method that uses aggregate ratings provided by various segments of users for various categories of items to derive better estimations of unknown individual ratings. This is achieved by converting the aggregate ratings into constraints on the parameters of a rating estimation model presented in the paper. The paper also demonstrates theoretically that these additional constraints reduce rating estimation errors resulting in better rating predictions.

REFERENCES

• 1: Gediminas Adomavicius , Ramesh Sankaranarayanan , Shahana Sen , Alexander Tuzhilin, Incorporating contextual information in recommender systems using a multidimensional approach, ACM Transactions on Information Systems (TOIS), v.23 n.1, p.103-145, January 2005 [doi>10.1145/1055709.1055714]
• 2: Gediminas Adomavicius , Alexander Tuzhilin, Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions, IEEE Transactions on Knowledge and Data Engineering, v.17 n.6, p.734-749, June 2005 [doi>10.1109/TKDE.2005.99]
• 3: A. Ansari, S. Essegaier, and R. Kohli. Internet recommendations systems. Journal of Marketing Research, 37(3), 2000.
• 4: Johan Bollen, Group User Models for Personalized Hyperlink Recommendations, Proceedings of the International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, p.38-50, August 28-30, 2000
• 5: M. Condliff, D. Lewis, D. Madigan, and C. Posse. Bayesian mixed-effects models for recommender systems. ACM SIGIR'99 Workshop on Recommender Systems: Algorithms and Evaluation, 15(5), 1999.
• 6: A. Gelfand and A. Smith. Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association, 85(410), 1990.
• 7: A. Gelfand, A. Smith, and T. Lee. Bayesian analysis of constrained parameter and truncated data problems using Gibbs sampling. Journal of the American Statistical Association, 87(418), 1992.
• 8: W. Greene. Econometric Analysis. Prentice Hall, 2002.
• 9: Ralph Kimball , Margy Ross, The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, John Wiley & Sons, Inc., New York, NY, 2002
• 10: Yuen Chung Kwong, Annual Review of Scalable Computing, World Scientific Publishing Co., Inc., River Edge, NJ, 2001
• 11: Mark O'Connor , Dan Cosley , Joseph A. Konstan , John Riedl, PolyLens: a recommender system for groups of users, Proceedings of the seventh conference on European Conference on Computer Supported Cooperative Work, p.199-218, September 16-20, 2001, Bonn, Germany
• 12: S. W. Raudenbush and A. S. Bryk. Hierarchical Linear Models: Applications and Data Analysis Methods. Sage Publications, Inc, 2001.
• 13: A. Umyarov and A. Tuzhilin. Leveraging aggregate ratings for better recommendations. Working paper. Stern School of Business. New York University. CeDER-07-03, 2007.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.1 MODELS AND PRINCIPLES
      H.1.2 User/Machine Systems
          Subjects: Human information processing

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering

General Terms:
Algorithms, Design, Theory

Keywords:
OLAP, aggregate ratings, hierarchical bayesian models, predictive models, recommender systems