Matrix Factorization Recommendation Algorithm Based on Multiple Social Relationships

With the widespread use of social networks, social recommendation algorithms that add social relationships between users to recommender systems have been widely applied. Existing social recommendation algorithms only introduced one type of social relationship to the recommendation system, but in reality, there are often multiple social relationships among users. In this paper, a new matrix factorization recommendation algorithm combined with multiple social relationships is proposed. Through experiment results analysis on the Epinions dataset, the proposed matrix factorization recommendation algorithm has a significant improvement over the traditional and matrix factorization recommendation algorithms that integrate a single social relationship.

[1]  T. Körpınar.  Optical directional binormal magnetic flows with geometric phase: Heisenberg ferromagnetic model , 2020 .

[2]  Sangwon Lee,et al.  Item-network-based collaborative filtering: A personalized recommendation method based on a user's item network , 2017, Inf. Process. Manag..

[3]  Gengxin Sun,et al.  基于多关系社交网络的协同过滤推荐算法 (Collaborative Filtering Recommendation Algorithm Based on Multi-relationship Social Network) , 2020, 计算机科学.

[4]  Shuai Li,et al.  An Efficient Approach to Generating Location-Sensitive Recommendations in Ad-hoc Social Network Environments , 2015, IEEE Transactions on Services Computing.

[5]  Kush R. Varshney,et al.  Collaborative Kalman Filtering for Dynamic Matrix Factorization , 2014, IEEE Transactions on Signal Processing.

[6]  Mehrbakhsh Nilashi,et al.  Hybrid recommendation approaches for multi-criteria collaborative filtering , 2014, Expert Syst. Appl..

[7]  Junpeng Chen,et al.  Topic sense induction from social tags based on non-negative matrix factorization , 2014, Inf. Sci..

[8]  Gengxin Sun,et al.  Construction of Learning Behavioral Engagement Model for MOOCs Platform Based on Data Analysis , 2018 .

[9]  Gengxin Sun,et al.  Router-Level Internet Topology Evolution Model based on Multi-Subnet Composited Complex Network Model , 2017 .

[10]  Kai Liu,et al.  Deep Item-based Collaborative Filtering for Top-N Recommendation , 2018, ACM Trans. Inf. Syst..

[11]  Ling Luo,et al.  Personalized recommendation by matrix co-factorization with tags and time information , 2019, Expert Syst. Appl..

[12]  Z. Korpinar,et al.  Soliton propagation of electromagnetic field vectors of polarized light ray traveling in a coiled optical fiber in the ordinary space , 2019, International Journal of Geometric Methods in Modern Physics.

[13]  Mahdi Jalili,et al.  A Time-Aware Recommender System Based on Dependency Network of Items , 2015, Comput. J..

[14]  D. Baleanu,et al.  Geometric phase for timelike spherical normal magnetic charged particles optical ferromagnetic model , 2020, Journal of Taibah University for Science.

[15]  T. Körpınar.  A new optical Heisenberg ferromagnetic model for optical directional velocity magnetic flows with geometric phase , 2019, Indian Journal of Physics.

[16]  Zhengtao Yu,et al.  A social recommendation method based on trust propagation and singular value decomposition , 2017, J. Intell. Fuzzy Syst..

[17]  Wei Chen,et al.  A hybrid approach of topic model and matrix factorization based on two-step recommendation framework , 2014, Journal of Intelligent Information Systems.