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Unified filtering by combining collaborative filtering and content-based filtering via mixture model and exponential model

Published: 13 November 2004 Publication History

Abstract

Collaborative filtering and content-based filtering are two types of information filtering techniques. Combining these two techniques can improve the recommendation effectiveness. The main problem with previous research is that the content information and the rating information are not combined in an integrated way. This paper presents a unified probabilistic framework that allows the mutual interaction between these two types of information. Experiments have shown that the new unified filtering algorithm outperforms a pure collaborative filtering approach, a pure content-based filtering approach and another unified filtering algorithm.

References

[1]
J. S. Breese, D. Heckerman & C. Kadie. (1998). Empirical Analysis of Predictive Algorthms for Collaborative Filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence.
[2]
P. Melville, R. Mooney & R. Nagarajan. (2002). Content-boosted collaborative filtering for improved recommendations. In Proceedings of Conference on Artificial Intelligence.
[3]
L. Si & R. Jin. (2003). Flexible Mixture Model for Collaborative Filtering. In Proceedings of the 20th International Conference on Machine Learning.
[4]
K. Yu, A. Schwaighofer, V. Tresp, W. Y. Ma & H. J. Zhang (2003). Collaborative ensemble learning: combining collaborative and content-based information filtering via Hierarchical Bayes. In Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence.

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  • (2021)KRAN: Knowledge Refining Attention Network for RecommendationACM Transactions on Knowledge Discovery from Data10.1145/347078316:2(1-20)Online publication date: 3-Sep-2021
  • (2016)An Efficient Recommendation Filter Model on Smart Home Big Data Analytics for Enhanced Living EnvironmentsSensors10.3390/s1610170616:10(1706)Online publication date: 15-Oct-2016
  • (2016)LTMF: Local-Based Tag Integration Model for RecommendationCollaborative Computing: Networking, Applications, and Worksharing10.1007/978-3-319-28910-6_27(296-302)Online publication date: 29-Jan-2016
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  1. Unified filtering by combining collaborative filtering and content-based filtering via mixture model and exponential model

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    cover image ACM Conferences
    CIKM '04: Proceedings of the thirteenth ACM international conference on Information and knowledge management
    November 2004
    678 pages
    ISBN:1581138741
    DOI:10.1145/1031171
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 13 November 2004

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    CIKM04: Conference on Information and Knowledge Management
    November 8 - 13, 2004
    D.C., Washington, USA

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    Cited By

    View all
    • (2021)KRAN: Knowledge Refining Attention Network for RecommendationACM Transactions on Knowledge Discovery from Data10.1145/347078316:2(1-20)Online publication date: 3-Sep-2021
    • (2016)An Efficient Recommendation Filter Model on Smart Home Big Data Analytics for Enhanced Living EnvironmentsSensors10.3390/s1610170616:10(1706)Online publication date: 15-Oct-2016
    • (2016)LTMF: Local-Based Tag Integration Model for RecommendationCollaborative Computing: Networking, Applications, and Worksharing10.1007/978-3-319-28910-6_27(296-302)Online publication date: 29-Jan-2016
    • (2015)Making recommendations from top-N user-item subgroupsNeurocomputing10.1016/j.neucom.2015.03.013165:C(228-237)Online publication date: 1-Oct-2015
    • (2014)Scalable Recommendation with Social Contextual InformationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2014.230048726:11(2789-2802)Online publication date: Nov-2014
    • (2014)Using Category and Keyword for Personalized Recommendation: A Scalable Collaborative Filtering Algorithm2014 Sixth International Symposium on Parallel Architectures, Algorithms and Programming10.1109/PAAP.2014.40(197-202)Online publication date: Jul-2014
    • (2013)A Multimedia Recommender SystemACM Transactions on Internet Technology10.1145/253264013:1(1-32)Online publication date: 1-Nov-2013
    • (2013)Scientific articles recommendationProceedings of the 22nd ACM international conference on Information & Knowledge Management10.1145/2505515.2505705(1147-1156)Online publication date: 27-Oct-2013
    • (2012)Social contextual recommendationProceedings of the 21st ACM international conference on Information and knowledge management10.1145/2396761.2396771(45-54)Online publication date: 29-Oct-2012
    • (2011)A Multimedia Semantic Recommender System for Cultural Heritage ApplicationsProceedings of the 2011 IEEE Fifth International Conference on Semantic Computing10.1109/ICSC.2011.47(403-410)Online publication date: 18-Sep-2011
    • Show More Cited By

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