Optimization Study of Personalized Information Recommendation Model Based on Tensor Decomposition

Hong Fei Sun; Xiao Dang Liu

doi:10.4028/www.scientific.net/AMR.1042.228

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 1042Optimization Study of Personalized Information...

Optimization Study of Personalized Information Recommendation Model Based on Tensor Decomposition

Abstract:

Based on the tensor decomposition especially pyramid decomposition method in matrix model, according to the high operation complexity of TD model, the author arises pairwise interaction tensor factorization (PITF) method to optimize it. And the tag recommendation, for example, this article simulate the interaction between all the labels on items a user tagging. The results show that on achieving expected quality test, PITF has obvious advantages than TD and CD at running time.

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Periodical:

Advanced Materials Research (Volume 1042)

Pages:

228-231

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1042.228

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Online since:

October 2014

Authors:

Hong Fei Sun*, Xiao Dang Liu

Keywords:

Optimization Study, Pairwise Interaction Tensor Factorization, Personalized Information Recommendation

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* - Corresponding Author

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