TY  - EJOU
AU  - Bin, Sheng  
AU  - Sun, Gengxin  
AU  - Cao, Ning  
AU  - Qiu, Jinming  
AU  - Zheng, Zhiyong  
AU  - Yang, Guohua  
AU  - Zhao, Hongyan  
AU  - Jiang, Meng  
AU  - Xu, Lina  

TI  - Collaborative Filtering Recommendation Algorithm Based on Multi-Relationship Social Network
T2  - Computers, Materials \& Continua

PY  - 2019
VL  - 60
IS  - 2
SN  - 1546-2226

AB  - Recommendation system is one of the most common applications in the field of big data. The traditional collaborative filtering recommendation algorithm is directly based on user-item rating matrix. However, when there are huge amounts of user and commodities data, the efficiency of the algorithm will be significantly reduced. Aiming at the problem, a collaborative filtering recommendation algorithm based on multi-relational social networks is proposed. The algorithm divides the multi-relational social networks based on the multi-subnet complex network model into communities by using information dissemination method, which divides the users with similar degree into a community. Then the user-item rating matrix is constructed by choosing the k-nearest neighbor set of users within the community, in this case, the collaborative filtering algorithm is used for recommendation. Thus, the execution efficiency of the algorithm is improved without reducing the accuracy of recommendation.
KW  - Big data
KW  -  recommendation algorithm
KW  -  social network
KW  -  recommendation efficiency

DO  - 10.32604/cmc.2019.05858