LTMF: Local-Based Tag Integration Model for Recommendation

Deyuan Zheng; Huan Huo; Shang-ye Chen; Biao Xu; Liang Liu

Collaborative Computing: Networking, Applications, and Worksharing. 11th International Conference, CollaborateCom 2015, Wuhan, November 10-11, 2015, China. Proceedings

Research Article

LTMF: Local-Based Tag Integration Model for Recommendation

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@INPROCEEDINGS{10.1007/978-3-319-28910-6_27,
    author={Deyuan Zheng and Huan Huo and Shang-ye Chen and Biao Xu and Liang Liu},
    title={LTMF: Local-Based Tag Integration Model for Recommendation},
    proceedings={Collaborative Computing: Networking, Applications, and Worksharing. 11th International Conference, CollaborateCom 2015, Wuhan, November 10-11, 2015, China. Proceedings},
    proceedings_a={COLLABORATECOM},
    year={2016},
    month={2},
    keywords={},
    doi={10.1007/978-3-319-28910-6_27}
}

Deyuan Zheng
Huan Huo
Shang-ye Chen
Biao Xu
Liang Liu
Year: 2016
LTMF: Local-Based Tag Integration Model for Recommendation
COLLABORATECOM
Springer
DOI: 10.1007/978-3-319-28910-6_27

Deyuan Zheng¹, Huan Huo¹^,*, Shang-ye Chen², Biao Xu¹, Liang Liu¹

1: University of Shanghai for Science and Technology
2: Northwest University

*Contact email: huo_huan@yahoo.com

Abstract

There are two primary approaches to collaborative filtering: memory- based and model-based. The traditional techniques fail to integrate with these two approaches and also can’t fully utilize the tag features which data contains. Based on mining local information, this paper combines neighborhood method and matrix factorization technique. By taking fuller consideration of the tag features, we propose an algorithm named LTMF (Local-Tag MF). After the real data validation, this model performs better than other state-of-art algorithms.

Published: 2016-02-09
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-319-28910-6_27