Visible to the public Implicit Trust Relation Extraction Based on Hellinger Distance

TitleImplicit Trust Relation Extraction Based on Hellinger Distance
Publication TypeConference Paper
Year of Publication2017
AuthorsXi, X., Zhang, F., Lian, Z.
Conference Name2017 13th International Conference on Semantics, Knowledge and Grids (SKG)
ISBN Number978-1-5386-2558-3
Keywordsactual explicit user trust ratings, clear trust data, computer theory, data mining, explicit social trust information, explicit trust data, gaussian distribution, graph theory, hellinger distance, Human Behavior, human factors, implicit social relation extractions, implicit social relationship, implicit social trust, implicit trust relation extraction, Matrix decomposition, matrix factorization, matrix factorization model, Measurement, Optimization, probability statistics, pubcrawl, Recommended system, recommender systems, social network, Social network services, social networking (online), social recommendation, social trust relationship extraction, Trust, trust measure methods, Trusted Computing, user similarity, user-item bipartite networks

Recent studies have shown that adding explicit social trust information to social recommendation significantly improves the prediction accuracy of ratings, but it is difficult to obtain a clear trust data among users in real life. Scholars have studied and proposed some trust measure methods to calculate and predict the interaction and trust between users. In this article, a method of social trust relationship extraction based on hellinger distance is proposed, and user similarity is calculated by describing the f-divergence of one side node in user-item bipartite networks. Then, a new matrix factorization model based on implicit social relationship is proposed by adding the extracted implicit social relations into the improved matrix factorization. The experimental results support that the effect of using implicit social trust to recommend is almost the same as that of using actual explicit user trust ratings, and when the explicit trust data cannot be extracted, our method has a better effect than the other traditional algorithms.

Citation Keyxi_implicit_2017