A Collaborative Filtering Algorithm Based on SVD and Trust Factor

DOI

10.2991/cnci-19.2019.5How to use a DOI?

Keywords

Collaborative filtering algorithm, singular value decomposition, trust factor, nearest neighbor.

Abstract

At present, most collaborative filtering algorithms use similarity as a criterion. In order to alleviate problems of cold start and sparsity in recommender system, a Collaborative Filtering Algorithm Combined with the Singular Value Decomposition (SVD) and Trust Factors (CFSVD-TF) is presented. Further mining data features, we use the SVD to mining data features to gain the implicit Items feature space, then the items-based similarity are computed by using the improved cosine similarity. The trust factor is integrated into the similarity space to generate the computable trust model. Finally, to evaluate the proposed CFSVD-TF approach, the accuracy of the CFSVD-TF algorithm has significantly improved than the traditional CF algorithm in MovieLens datasets.

Copyright

© 2019, 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  - Jianfang Wang
AU  - Pengfei Han
AU  - Yanling Miao
AU  - Fengming Zhang
PY  - 2019/05
DA  - 2019/05
TI  - A Collaborative Filtering Algorithm Based on SVD and Trust Factor
BT  - Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019)
PB  - Atlantis Press
SP  - 33
EP  - 39
SN  - 2352-538X
UR  - https://doi.org/10.2991/cnci-19.2019.5
DO  - 10.2991/cnci-19.2019.5
ID  - Wang2019/05
ER  -