Personalized Recommendation Algorithm of Multi-fac

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[Objective] This paper aims to solve the low accuracy issue facing personalized recommendation algorithm of multi-faceted trust tensor based on tag clustering. [Methods] First, we proposed a new method to calculate multi-faceted trust based on tag clusters. Then, we introduced the TF-IDF and Pearson similarity to indicate strength of inter-cluster and intra-cluster trust. Finally, we built recommendation mechanism based on tensor decomposition to reflect the trust intensity from different facets. [Results] We examined the new algorithm with the Last.fm dataset. The precision, recall and F1 measures were better than traditional methods. Among them, the F1 measure was increased by 2.29% on average. [Limitations] Our new algorithm needs to be examined with datasets from Weibo or Twitter. [Conclusions] The proposed algorithm could effectively increase the accuracy of recommendation by defining and quantifying trust relationship among users. It improves the user experience of social network systems.

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