A Collaborative Filtering Algorithm based on Improved Similarity

DOI

10.2991/mecae-17.2017.31How to use a DOI?

Keywords

Collaborative Filtering, Movie Recommendation, Pearson Similarity.

Abstract

The collaborative filtering recommendation implements recommendation by using his neighbor user's preference. And the similarity calculation is the key. The traditional similarity calculation neglects the impact of co-rating item number and user average rating on similarity calculation. This causes the poor similarity calculation of users in case of sparse data. This paper introduces the two improved factors to the improved algorithm, so as to improve the traditional similarity. Meanwhile, the improved recommendation algorithm has been applied to film recommendation system. The simulation experiment shows that the improved recommendation algorithm can get a lower MAE value than traditional recommendation algorithm. In addition, the improved algorithm can improve the quality of film recommendation system.

Copyright

© 2017, 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  - Yan Zhou
PY  - 2017/03
DA  - 2017/03
TI  - A Collaborative Filtering Algorithm based on Improved Similarity
BT  - Proceedings of the 2017 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2017)
PB  - Atlantis Press
SP  - 168
EP  - 171
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecae-17.2017.31
DO  - 10.2991/mecae-17.2017.31
ID  - Zhou2017/03
ER  -