[Objective] This paper tries to improve user similarity calculation in collaborative filtering recommendation with trust relationship among them. Once there is no similar user for members of the target group, we recommend the most trusted ones as the similar users. [Methods] First, we retrieved the trusted users as candidates for the similar users. Second, we combined the trusted and the target users to form a project score set, and evaluated the estimated value of the projects receiving no comment from the target group. Third, we quantified the trust relationship among users to form a regulation factor. Finally, we calculated the adjustment factor and created the similarity cluster of users, and made cross-recommendation among similar users. [Results] The collaborative filtering recommendation method based on trust relationship had better performance than traditional ones. [Limitations] Only examined the new method with one sample dataset with trusted relationship. More research is needed to test the proposed method with other datasets. [Conclusions] The trusted relationship among users contains valuable information, which could be used to calculate user similarity for collaborative filtering recommendation services, and then effectively solves the sparsity and cold start issue.
Wang Y, Singh M P.Formal Trust Model for Multiagent System[C]//Proceedings of the 20th International Joint Conference on Artificial Intelligence.2007: 1551-1556.
Lampropoulos A S, Lampropoulos P S, Tsihrintzis G A.A Cascade-Hybrid Music Recommender System for Mobile Services Based on Musical Genre Classification and Personality Diagnosis[J]. Multimedia Tools and Applications, 2012, 59(1): 241-258.
Shambour Q, Lu J.A Trust-semantic Fusion-based Recommendation Approach for E-business Application[J]. Decision Support Systems, 2012, 54(1): 768-780.
Adomavicius G, Tuzhilin A.Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-art and Possible Extensions[J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734-749.
Jøsang A, Quattrociocchi W, Karabeg D.Taste and Trust[C]//Proceedings of IFIP International Conference on Trust Management.2011: 312-322.
Chowdhury M, Thomo A, Wadge W W.Trust-based Infinitesimals for Enhanced Collaborative Filtering[C]// Proceedings of the 15th International Conference on Management of Data. 2009.
Danis C, Singer D.A Wiki Instance in the Enterprise: Opportunities, Concerns and Reality[C]//Proceedings of the 2008 ACM Conference on Computer Supported Cooperative Work.2008: 495-504.
De Rosa C, Cantrell J, Havens A, et al.Sharing, Privacy and Trust in Our Networked World[R]. A Report to the OCLC Membership, OCLC, 2007.
Golbeck J A.Computing and Applying Trust in Web-Based Social Networks[D]. College Park, MD, USA: University of Maryland at College Park, 2005.
(Zou Benyou, Li Cuiping, Tan Liwen, et al.Social Recommendations Based on User Trust and Tensor Factorization[J]. Journal of Software, 2014, 25(12): 2852-2864.)
Guo G, Zhang J, Thalmann D.Merging Trust in Collaborative Filtering to Alleviate Data Sparsity and Cold Start[J]. Knowledge-Based Systems, 2014, 57: 57-68.
Ma X, Lu H, Gan Z, et al.An Explicit Trust and Distrust Clustering Based Collaborative Filtering Recommendation Approach[J]. Electronic Commerce Research and Applications, 2017, 25: 29-39.
Jia D, Zhang F, Liu S.A Robust Collaborative Filtering Recommendation Algorithm Based on Multidimensional Trust Model[J]. Journal of Software, 2013, 8(1): 11-18.
Xu X L, Xu G L.Improved Collaborative Filtering Recommendation Based on Classification and User Trust[J]. Journal of Electronic Science and Technology, 2016, 14(1): 25-31.
Du Y, Du X, Huang L.Improve the Collaborative Filtering Recommender System Performance by Trust Network Construction[J]. Chinese Journal of Electronics, 2016, 25(3): 418-423.
Jamali M, Ester M.Trustwalker: A Random Walk Model for Combining Trust-based and Item-based Recommendation[C]// Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2009: 397-406.
Diaz-Aviles E, Drumond L, Schmidt-Thieme L, et al.Real-time Top-n Recommendation in Social Streams[C]// Proceedings of the 6th ACM Conference on Recommender Systems.2012: 59-66.
(Li Cong, Liang Changyong, Dong Ke.A Collaborative Filtering Recommendation Algorithm Based on Item Category Similarity[J]. Journal of Hefei University of Technology: Natural Science Edition, 2008, 31(3): 360-363.)