%A Chen Meimei,Xue Kangjie %T Personalized Recommendation Algorithm Based on Modified Tensor Decomposition Model %0 Journal Article %D 2017 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2017.03.05 %P 38-45 %V 1 %N 3 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_3146.shtml} %8 2017-03-25 %X

[Objective] This paper tries to improve the prediction accuracy of personalized recommendation algorithm based on the tensor decomposition model. [Methods] First, we proposed a new tensor model using spectral clustering technique based on combined tag co-occurrence. Second, we established a penalty scheme on popular tag and resource co-occurrence with the help of IDF in TF-IDF. Finally,we re-defined the initial tensor on the triplets of user, tag cluster, and resource. [Results] We examined the proposed model with dataset from Last.fm and found its precision, recall and F1 measure outperformed other algorithms. The F1 measures were increased by 5.91% and 1.29% thanks to the two proposed modifictions based on clustering and IDF. [Limitations] The proposed algorithm should be further evaluated with datasets from Weibo, Delicious, and other resources. [Conclusions] The new algorithm based on advanced tensor decomposition model could significantly improve the accuracy of resources recommendation to satisfy social network system users’ information needs.