Article

A Hybrid Collaborative Filtering Algorithm Based on User-Item

Authors:

Yan-ni Chen,

Min YuAuthors Info & Claims

ICCIS '10: Proceedings of the 2010 International Conference on Computational and Information Sciences

Pages 618 - 621

https://doi.org/10.1109/ICCIS.2010.156

Published: 17 December 2010 Publication History

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Abstract

Collaborative filtering is one of the most important technologies in e-commerce recommendation system. Traditional similarity measure methods work poorly when the user rating data are extremely sparse. Aiming at this issue a hybrid collaborative filtering is proposed. This method used a novel similarity measure method to predict the target item rating and it fused the advantages of the user-based algorithm and item-based algorithm with the control factor α. The experimental results show that this improved algorithm obviously enhances the recommended accuracy, and provide better recommendation quality.

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Duan RJiang CJain H(2022)Combining review-based collaborative filtering and matrix factorizationDecision Support Systems10.1016/j.dss.2022.113748156:COnline publication date: 1-May-2022
https://dl.acm.org/doi/10.1016/j.dss.2022.113748
Krauss CGeorge LArbanowski SFan WBifet AYang QYu P(2013)TV predictorProceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications10.1145/2501221.2501230(63-70)Online publication date: 11-Aug-2013
https://dl.acm.org/doi/10.1145/2501221.2501230
Bilge APolat H(2013)A scalable privacy-preserving recommendation scheme via bisecting k-means clusteringInformation Processing and Management: an International Journal10.1016/j.ipm.2013.02.00449:4(912-927)Online publication date: 1-Jul-2013
https://dl.acm.org/doi/10.1016/j.ipm.2013.02.004

A Hybrid Collaborative Filtering Algorithm Based on User-Item
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals

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Published In

ICCIS '10: Proceedings of the 2010 International Conference on Computational and Information Sciences

December 2010

1376 pages

ISBN:9780769542706

Publisher

IEEE Computer Society

United States

Publication History

Published: 17 December 2010

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Duan RJiang CJain H(2022)Combining review-based collaborative filtering and matrix factorizationDecision Support Systems10.1016/j.dss.2022.113748156:COnline publication date: 1-May-2022
https://dl.acm.org/doi/10.1016/j.dss.2022.113748
Krauss CGeorge LArbanowski SFan WBifet AYang QYu P(2013)TV predictorProceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications10.1145/2501221.2501230(63-70)Online publication date: 11-Aug-2013
https://dl.acm.org/doi/10.1145/2501221.2501230
Bilge APolat H(2013)A scalable privacy-preserving recommendation scheme via bisecting k-means clusteringInformation Processing and Management: an International Journal10.1016/j.ipm.2013.02.00449:4(912-927)Online publication date: 1-Jul-2013
https://dl.acm.org/doi/10.1016/j.ipm.2013.02.004

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Generating Items Recommendations by Fusing Content and User-Item based Collaborative Filtering

A new similarity function for selecting neighbors for each target item in collaborative filtering

Generating Top-N Items Recommendation Set Using Collaborative, Content Based Filtering and Rating Variance

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