Computer Science ›› 2020, Vol. 47 ›› Issue (2): 65-71.doi: 10.11896/jsjkx.190200362

• Database & Big Data & Data Science • Previous Articles     Next Articles

Mining Deep Semantic Features of Reviews for Amazon Commodity Recommendation

LI Ke1,CHEN Guang-ping2   

  1. (Chongqing Research Academy of Education Sciences,Chongqing 400015,China)1;
    (College of Informatica Engineering,China Jiliang University,Hangzhou 310018,China)2
  • Received:2019-02-26 Online:2020-02-15 Published:2020-03-18
  • About author:LI Ke,born in 1977.His main research interests include information technology education and AI education.
  • Supported by:
    This work was supported by the research program of Chongqing Education Science “13th Five-Year” Plan (2016-00-011) and Particular Research Program of Chongqing University of Education (KY2018TZ03).

Abstract: Review mining plays an important role in the field of recommender system (RS).However,conventional mining methodscannot explicitly mine deep semantic features of reviews.Therefore,the major challenge in RS is how to mine deep semantics of reviews.This paper utilized Skip-Thought Vectors (STV) to learn latent semantic features of reviews.In addition,in order to enhance the ability of semantic representation of reviews,it introduced the Long Short-Term Memory (LSTM) network into STV,and proposed a deeply hierarchical bi-directional feature-extraction model in combination with bi-directional information mining method,user preference mining method and deeply hierarchical model.The introduced model can not only mine the deep semantic feature of reviews,but also mine the user’s emotional preferences.Then,the proposed model is combined with the Singular Value Decomposition (SVD) model.Experiments on two Amazon datasets show that the proposed model performs better than conventional models due to its strong ability of deep semantics mining of reviews.

Key words: Commodity recommendation, Deep learning, Semantic mining, Singular value decomposition, Text representation

CLC Number: 

  • TP391
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