Proceedings of the 2017 6th International Conference on Measurement, Instrumentation and Automation (ICMIA 2017)

A Hybrid Recommendation Algorithm Based on Social and Collaborative Filtering

Authors
Li Guo, Yijun Yang, Rong Huang
Corresponding Author
Li Guo
Available Online June 2017.
DOI
https://doi.org/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.
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Proceedings
2017 6th International Conference on Measurement, Instrumentation and Automation (ICMIA 2017)
Part of series
Advances in Intelligent Systems Research
Publication Date
June 2017
ISBN
978-94-6252-387-6
ISSN
1951-6851
DOI
https://doi.org/10.2991/icmia-17.2017.45How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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  - 2017 6th International Conference on Measurement, Instrumentation and Automation (ICMIA 2017)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmia-17.2017.45
DO  - https://doi.org/10.2991/icmia-17.2017.45
ID  - Guo2017/06
ER  -