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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (3): 38-45    DOI: 10.11925/infotech.2096-3467.2017.03.05
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Personalized Recommendation Algorithm Based on Modified Tensor Decomposition Model
Chen Meimei(), Xue Kangjie
Glorious Sun School of Business & Management, Donghua University, Shanghai 200051, China
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Abstract  

[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.

Key wordsPersonalized Recommendation      UGC      Tag      Tag Co-occurrence      Spectral Clustering      Tensor Decomposition     
Received: 10 November 2016      Published: 25 September 1985
ZTFLH:  F224.39 TP391 TP181  

Cite this article:

Chen Meimei,Xue Kangjie. Personalized Recommendation Algorithm Based on Modified Tensor Decomposition Model. Data Analysis and Knowledge Discovery, 2017, 1(3): 38-45.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.03.05     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I3/38

数据集 准确性指标 CoSCluIDF CoSClu KmeansIDF TD
Tag8 Precision 22.69% 22.56% 22.38% 11.39%
Recall 43.61% 43.36% 42.94% 21.32%
F1 28.21% 28.05% 27.80% 13.99%
Tag20 Precision 23.67% 23.05% 22.54% 12.20%
Recall 45.80% 44.59% 43.59% 23.05%
F1 29.48% 28.71% 28.07% 15.03%
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