An Improved Singular Value Decomposition Recommender Algorithm Based on User Trust Relationship

DOI

10.2991/icmii-15.2015.105How to use a DOI?

Keywords

recommender algorithm; collaborative filtering; trust; singular value decomposition

Abstract

Collaborative filtering is one of the most widely used recommender algorithms, whereas it is suffering the issues of data sparsity. Recommender algorithms based on trust perform better in alleviating data sparsity. However, there remain shortages in the process of mining trust relation in specific algorithms, which limit the improvement of prediction accuracy. To address this problem, the paper proposes an improved singular value decomposition algorithm, trying to integrate truster-specific and trustee-specific information and the implicit feedback of each when generating predictions. Experiments on the Epinions dataset show that the proposed algorithm performs better than state-of-the-art recommender algorithms in prediction accuracy.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Junchi Pan
AU  - Xingming Zhang
AU  - Xiaofeng Qi
PY  - 2015/10
DA  - 2015/10
TI  - An Improved Singular Value Decomposition Recommender Algorithm Based on User Trust Relationship
BT  - Proceedings of the 3rd International Conference on Mechatronics and Industrial Informatics
PB  - Atlantis Press
SP  - 617
EP  - 622
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmii-15.2015.105
DO  - 10.2991/icmii-15.2015.105
ID  - Pan2015/10
ER  -