ABSTRACT
Collaborative filtering (CF) is valuable in e-commerce, and for direct recommendations for music, movies, news etc. But today's systems have several disadvantages, including privacy risks. As we move toward ubiquitous computing, there is a great potential for individuals to share all kinds of information about places and things to do, see and buy, but the privacy risks are severe. In this paper we describe a new method for collaborative filtering which protects the privacy of individual data. The method is based on a probabilistic factor analysis model. Privacy protection is provided by a peer-to-peer protocol which is described elsewhere, but outlined in this paper. The factor analysis approach handles missing data without requiring default values for them. We give several experiments that suggest that this is most accurate method for CF to date. The new algorithm has other advantages in speed and storage over previous algorithms. Finally, we suggest applications of the approach to other kinds of statistical analyses of survey or questionaire data.
- C. Basu, H. Hirsh, and W. W. Cohen. Recommendation as classification: Using social and content-based information in recommendation. In AAAI/IAAI, pages 714--720, 1998. Google ScholarDigital Library
- Breese, Heckermen, and Kadie. Empirical analysis of predictive algorithms for collaborative filtering. Technical report, Microsoft Research, October 1998.Google Scholar
- J. Canny. Collaborative filtering with privacy. In IEEE Symposium on Security and Privacy, pages 45--57, Oakland, CA, May 2002. Google ScholarDigital Library
- J. Canny. Some techniques for privacy in ubicomp and context-aware applications. In UBICOMP-2002, Goteborg, Sweden, Sept. 2002. (submitted).Google Scholar
- M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes, and M. Sartin. Combining content-based and collaborative filters in an online newspaper. In ACM SIGIR WS on Recommender Systems, 1999.Google Scholar
- A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 39(1):1--38, 1977.Google ScholarCross Ref
- J. DeTreville, 2002. personal communication.Google Scholar
- B. Frey. Turbo factor analysis. Adv. Neural Information Processing, 1999. (submitted).Google Scholar
- Z. Ghahramani and M. I. Jordan. Learning from incomplete data. Technical Report AIM-1509, MIT AI Lab, 1994. Google ScholarDigital Library
- K. Goldberg, D. Gupta, M. Digiovanni, and H. Narita. Jester 2.0 : Evaluation of a new linear time collaborative filtering algorithm. In ACM SIGIR, August 1999. Poster Session and Demonstration. Google ScholarDigital Library
- N. Good, J. B. Schafer, J. A. Konstan, A. Borchers, B. M. Sarwar, J. L. Herlocker, and J. Riedl. Combining collaborative filtering with personal agents for better recommendations. In AAAI/IAAI, pages 439--446, 1999. Google ScholarDigital Library
- J. Herlocker, J. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In Proc. ACM SIGIR, 1999. Google ScholarDigital Library
- O. P. John. The "big five" factor taxonomy: Dimensions of personality in the natural language and in questionnaires. In L. A. Pervin, editor, Handbook of personality: Theory and research. Guilford, NY, 1990.Google Scholar
- M. Jordan and C. Bishop. An Introduction to Graphical Models. MIT Press, 2002. In press.Google Scholar
- J. Kubiatowicz, D. Bindel, Y. Chen, S. Czerwinski, P. Eaton, D. Geels, R. Gummadi, S. Rhea, H. Weatherspoon, W. Weimer, C. Wells, and B. Zhao. Oceanstore: An architecture for global-scale persistent storage. In ACM ASPLOS, November 2000. Google ScholarDigital Library
- D. Pennock and E. Horvitz. Collaborative filtering by personality diagnosis: A hybrid memory- and model-based approach. In IJCAI Workshop on Machine Learning for Information Filtering, Stockholm, Sweden, August 1999.Google Scholar
- A. Popescul, L. Ungar, D. Pennock, and S. Lawrence. Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In 17th Conference on Uncertainty in Artificial Intelligence, Seattle, WA, August 2001. Google ScholarDigital Library
- E. M. Rogers. Diffusion of Innovations, Fourth Edition. The Free Press, 1995.Google Scholar
- B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl. Application of dimensionality reduction in recommender system -- a case study. In ACM WebKDD 2000 Web Mining for E-Commerce Workshop, 2000. Full length paper.Google ScholarCross Ref
Index Terms
- Collaborative filtering with privacy via factor analysis
Recommendations
Integrating collaborative filtering and matching-based search for product recommendations
Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and, thus, help in making good ...
An algorithm for efficient privacy-preserving item-based collaborative filtering
Collaborative filtering (CF) methods are widely adopted by existing recommender systems, which can analyze and predict user "ratings" or "preferences" of newly generated items based on user historical behaviors. However, privacy issue arises in this ...
Trust-based collaborative filtering: tackling the cold start problem using regular equivalence
RecSys '18: Proceedings of the 12th ACM Conference on Recommender SystemsUser-based Collaborative Filtering (CF) is one of the most popular approaches to create recommender systems. This approach is based on finding the most relevant k users from whose rating history we can extract items to recommend. CF, however, suffers ...
Comments