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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (5): 94-101    DOI: 10.11925/infotech.2096-3467.2017.05.11
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Personalized Recommendation Algorithm of Multi-faceted Trust Tensor Based on Tag Clustering
Meimei Chen(),Kangjie Xue
Glorious Sun School of Business & Management, Donghua University, Shanghai 200051, China
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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 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.

Key wordsPersonalized Recommendation      UGC Tag      Tensor Decomposition      Multi-faceted Trust     
Received: 20 February 2017      Published: 06 June 2017

Cite this article:

Meimei Chen,Kangjie Xue. Personalized Recommendation Algorithm of Multi-faceted Trust Tensor Based on Tag Clustering. Data Analysis and Knowledge Discovery, 2017, 1(5): 94-101.

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