The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Collaborative filtering with privacy via factor analysis
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.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
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Cited by
Evaluating collaborative filtering recommender systems
Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004) · ACM Transactions on Information Systems · doi:10.1145/963770.963772 · Cited by 5,798
Introduction to Recommender Systems Handbook
Ricci, F., Rokach, L., & Shapira, B. (2011) · Recommender Systems Handbook · doi:10.1007/978-0-387-85820-3_1 · Cited by 2,378
Restricted Boltzmann machines for collaborative filtering
Salakhutdinov, R., Mnih, A., & Hinton, G. (2007) · Proceedings of the 24th international conference on Machine learning · doi:10.1145/1273496.1273596 · Cited by 1,885
Feature selection, L 1 vs. L 2 regularization, and rotational invariance
Ng, A. Y. (2004) · Twenty-first international conference on Machine learning - ICML '04 · doi:10.1145/1015330.1015435 · Cited by 1,572
Personalization and privacy: a survey of privacy risks and remedies in personalization-based systems
Toch, E., Wang, Y., & Cranor, L. F. (2012) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-011-9110-z · Cited by 226
Metadata
- Authors
- Canny, J.
- Venue
- Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2002-08-11
- Pages
- 238-245
- OpenAlex
- W2070786785
- Cited by
- 508
- References
- 1
- FWCI
- 8.93
- Type
- conference paper
- Language
- en
- Open access
- closed
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