The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Probabilistic memory-based collaborative filtering
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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Cites
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2005) · doi:10.1002/047174882x · Cited by 13,791
Explaining collaborative filtering recommendations
Herlocker, J. L., Konstan, J. A., & Riedl, J. (2000) · Proceedings of the 2000 ACM conference on Computer supported cooperative work · doi:10.1145/358916.358995 · Cited by 1,731
Metadata
- Authors
- Yu, K., Schwaighofer, A., Tresp, V., Xu, X., & Kriegel, H.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2004-01
- Volume
- 16
- Issue
- 1
- Pages
- 56-69
- ISSN
- 1041-4347 (ISSN-L)
- OpenAlex
- W2139555631
- Cited by
- 292
- References
- 2
- FWCI
- 22.29
- Type
- journal article
- Language
- en
- Open access
- closed
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- Yu, K., Schwaighofer, A., Tresp, V., Xu, X., & Kriegel, H. (2004). Probabilistic memory-based collaborative filtering. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/tkde.2004.1264822
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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