A User Interest Recommendation Based on Collaborative Filtering

DOI

10.2991/aiie-16.2016.122How to use a DOI?

Keywords

collaborative filtering; Bhattacharyya Coefficient; forgetting curve; interest weight; similarity

Abstract

The traditional collaborative filtering algorithm cannot response user interest with time and is lack of time effectiveness. These problems lead to poor recommendation quality. On the basis of the neighbor-based collaborative filtering, a fused method of improved similarity and user interest is proposed. To begin with, we compute similarity from global perspectives obtained with Jaccard similarity, local perspectives obtained with Bhattacharyya Coefficient. Furthermore, we adopt the forgetting curve to represent the user interest preference, adding the interest weight to the new similarity method to update user interest. Finally, we make recommendation prediction by calculating similarity using the method. Experimental results on the Movielens datasets demonstrate that our approach has advantages over state-of-the-art methods in terms of both the discovery of user interest preference and providing highly accuracy recommendations.

Copyright

© 2016, 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  - Wenqi Wu
AU  - Jianfang Wang
AU  - Randong Liu
AU  - Zhenpeng Gu
AU  - Yongli Liu
PY  - 2016/11
DA  - 2016/11
TI  - A User Interest Recommendation Based on Collaborative Filtering
BT  - Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
PB  - Atlantis Press
SP  - 524
EP  - 528
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-16.2016.122
DO  - 10.2991/aiie-16.2016.122
ID  - Wu2016/11
ER  -