A Hybrid Recommendation Algorithm Based on Social and Collaborative Filtering

DOI

10.2991/icmia-17.2017.45How to use a DOI?

Keywords

item-based collaborative filtering; social relationships; hybrid recommendation

Abstract

Social-based recommendation and collaborative filtering-based recommendation have their own characteristics. Considering that traditional collaborative filtering only makes use of users' behavior data but ignores users' social relationships, a recommendation algorithm combined with social and collaborative filtering was proposed in this paper. Traditional item-based collaborative filtering algorithm was improved first, and then the hybrid recommendation algorithm was constructed by considering the complementarity of users' behavior data and social relationships, which can relieve the existing problems of collaborative filtering such as data sparse and cold start and is proved to improve the accuracy of the recommendation though experiment.

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  - Li Guo
AU  - Yijun Yang
AU  - Rong Huang
PY  - 2017/06
DA  - 2017/06
TI  - A Hybrid Recommendation Algorithm Based on Social and Collaborative Filtering
BT  - Proceedings of the 2017 6th International Conference on Measurement, Instrumentation and Automation (ICMIA 2017)
PB  - Atlantis Press
SP  - 242
EP  - 247
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmia-17.2017.45
DO  - 10.2991/icmia-17.2017.45
ID  - Guo2017/06
ER  -