A Hybrid Recommendation Algorithm Based on Social and Collaborative Filtering

被引：0
|
作者：
Li, Guo [1 ]
Yijun, Yang [2 ]
Rong, Huang [2 ]
机构：
[1] Wuhan Elect Power Tech Coll, HBEPC Technol Training Ctr, Dept Informat Engn, Wuhan 430079, Hubei, Peoples R China
[2] Wuhan Univ Technol, Sch Informat Engn, Wuhan 430070, Hubei, Peoples R China
来源：
PROCEEDINGS OF THE 2017 6TH INTERNATIONAL CONFERENCE ON MEASUREMENT, INSTRUMENTATION AND AUTOMATION (ICMIA 2017) | 2017年 / 154卷
关键词：
item-based collaborative filtering; social relationships; hybrid recommendation;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：242 / 247
页数：6
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