期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 28, 期 7, 页码 1607-1620出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2016.2528249
关键词
Recommender systems; social trust; matrix factorization; implicit trust; collaborative filtering
类别
资金
- MoE AcRF Tier 2 Grant [M4020110.020]
- National Natural Science Foundation of China [61402097]
- St Edmund's College, Cambridge
We propose Trust SVD, a trust-based matrix factorization technique for recommendations. TrustSVD integrates multiple information sources into the recommendation model in order to reduce the data sparsity and cold start problems and their degradation of recommendation performance. An analysis of social trust data from four real-world data sets suggests that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. TrustSVD therefore builds on top of a state-of-the-art recommendation algorithm, SVD++ (which uses the explicit and implicit influence of rated items), by further incorporating both the explicit and implicit influence of trusted and trusting users on the prediction of items for an active user. The proposed technique is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that TrustSVD achieves better accuracy than other ten counterparts recommendation techniques.
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