The Recommendation System Based on Semi-Supervised PSO Clustering Algorithm

DOI

10.2991/ifmca-16.2017.11How to use a DOI?

Keywords

Collaborative Filtering, Recommendation System, Particle Swarm Optimization, Semi-Supervised Clustering.

Abstract

Recommendation system has been one of the subject of intense in Computer Science, and it is widely used in Information Science. Various types of e-commerce systems and a large number of Internet applications need to use recommendation system to support their service. Recommendation system is aimed to provide users with the most valuable reference information. It filters out a large amount of useless information to help users to shorten the time to make a decision. A good recommendation system can accurately put the information to the specific users, and can accurately predict the users' behavior. In recent years, collaborative filtering recommendation algorithm has made a great progress. Among them, the performance of Semi-Supervised PSO clustering algorithm has been greatly improved to the traditional clustering methods. This paper tries to combine the Semi-Supervised PSO clustering algorithm with the clustering process of the recommendation system, and compare the performance of the new recommendation system with the old recommendation system based on the traditional clustering algorithm. The accuracy and effectiveness of the recommendation algorithm are validated by the experimental data.

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  - Wenmin Zhou
AU  - Xiuqin Pan
AU  - Ruixiang Li
AU  - Yong Lu
PY  - 2017/03
DA  - 2017/03
TI  - The Recommendation System Based on Semi-Supervised PSO Clustering Algorithm
BT  - Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)
PB  - Atlantis Press
SP  - 63
EP  - 71
SN  - 2352-5401
UR  - https://doi.org/10.2991/ifmca-16.2017.11
DO  - 10.2991/ifmca-16.2017.11
ID  - Zhou2017/03
ER  -