Journal Article10.1109/TKDE.2004.1264822
Probabilistic memory-based collaborative filtering
TL;DR: It is shown that a probabilistic active learning method can be used to actively query the user, thereby solving the "new user problem" of memory-based collaborative filtering.
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Abstract: Memory-based collaborative filtering (CF) has been studied extensively in the literature and has proven to be successful in various types of personalized recommender systems. In this paper, we develop a probabilistic framework for memory-based CF (PMCF). While this framework has clear links with classical memory-based CF, it allows us to find principled solutions to known problems of CF-based recommender systems. In particular, we show that a probabilistic active learning method can be used to actively query the user, thereby solving the "new user problem." Furthermore, the probabilistic framework allows us to reduce the computational cost of memory-based CF by working on a carefully selected subset of user profiles, while retaining high accuracy. We report experimental results based on two real-world data sets, which demonstrate that our proposed PMCF framework allows an accurate and efficient prediction of user preferences.
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References
•Book
Elements of information theory
Thomas M. Cover,Joy A. Thomas +1 more
- 01 Jan 1991
TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.
Item-based collaborative filtering recommendation algorithms
Badrul Sarwar,George Karypis,Joseph A. Konstan,John Riedl +3 more
- 01 Apr 2001
TL;DR: This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
GroupLens: an open architecture for collaborative filtering of netnews
Paul Resnick,Neophytos Iacovou,Mitesh Suchak,Peter Bergstrom,John Riedl +4 more
- 22 Oct 1994
TL;DR: GroupLens is a system for collaborative filtering of netnews, to help people find articles they will like in the huge stream of available articles, and protect their privacy by entering ratings under pseudonyms, without reducing the effectiveness of the score prediction.
6K
•Proceedings Article
Empirical analysis of predictive algorithms for collaborative filtering
John S. Breese,David Heckerman,Carl M. Kadie +2 more
- 24 Jul 1998
TL;DR: Several algorithms designed for collaborative filtering or recommender systems are described, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods, to compare the predictive accuracy of the various methods in a set of representative problem domains.
•Posted Content
Empirical Analysis of Predictive Algorithms for Collaborative Filtering
TL;DR: In this article, the authors compare the predictive accuracy of various methods in a set of representative problem domains, including correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods.
5.1K