Factored similarity models with social trust for top-N item recommendation
Trust-aware recommender systems have attracted much attention recently due to the prevalence of social networks. However, most existing trust-based approaches are designed for the recommendation task of rating prediction. Only few trust-aware methods have attempted to recommend users an ordered list of interesting items, i.e., item recommendation. In this article, we propose three factored similarity models with the incorporation of social trust for item recommendation based on implicit user feedback. Specifically, we introduce a matrix factorization technique to recover user preferences between rated items and unrated ones in the light of both user-user and item-item similarities. In addition, we claim that social trust relationships also have an important impact on a user’s preference for a specific item. Experimental results on three real-world data sets demonstrate that our approach achieves superior ranking performance to other counterparts.
Recommender Systems, Matrix Factorization, Social Trust, Trust Influence
Databases and Information Systems | E-Commerce
Data Management and Analytics
GUO, Guibing; ZHANG, Jie; ZHU, Feida; and WANG, Xingwei.
Factored similarity models with social trust for top-N item recommendation. (2017). Knowledge-based Systems. 122, 17-25. Research Collection School of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research_all/12
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