A Hybrid Recommendation Algorithm Based on Social and Collaborative Filtering
Li Guo, Yijun Yang, Rong Huang
Available Online June 2017.
- https://doi.org/10.2991/icmia-17.2017.45How to use a DOI?
- item-based collaborative filtering; social relationships; hybrid recommendation
- 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.
- 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 -