Computer Science

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).

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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
[1]SCHAFER J B,KONSTAN J A,RIEDL J.E-Commerce Recommendation Applications[J].Data Mining and Knowledge Disco-very,2001,5(1):115-153.
[2]LINDEN G,SMITH B,YORK J.Amazon.com Recommendations:Item-to-Item Collaborative Filtering[J].IEEE Internet Computing,2003,7(1):76-80.
[3]HORRIGAN J A.Online shopping.In Pew Internet & American Life Project Report [OL] https://www.pewinternet.org/2008/02/13/online-shopping-2/.
[4]HERLOCKER J L,KONSTAN J A,TERVEEN L G,et al. Evaluating collaborative filtering recommender systems[J].ACM Transactions on Information Systems (TOIS),2004,22(1):5-53.
[5]CAMPOS L M D,FERNÁNDEZ-LUNA J M,HUETE J F,et al.Combining content-based and collaborative recommendations:A hybrid approach based on Bayesian networks[J].International Journal of Approximate Reasoning,2010,51(7):785-799.
[6]LIANG C Y,LENG Y J,WANG Y S,et al.Research on Group Recommendation in E-commerce Recommender Systems[J].Chinese Journal of Management Science,2013(3):153-158.
[7]GANU G,ELHADAD N,MARIAN A.Beyond the Stars:Improving Rating Predictions using Review Text Content [C]∥Conference:12th International Workshop on the Web and Databases,WebDB 2009.Rhode Island,Usa,2009,9:1-6.
[8]DIAO Q,QIU M,WU C Y,et al.Jointly modeling aspects,ra-tings and sentiments for movie recommendation (jmars)[C]∥Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2014:193-202.
[9]LE Q V,MIKOLOV T.Distributed Representations of Sen-tences and Documents [C]∥International Conference on Machine Learning.2014,4:1188-1196.
[10]HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural Computation,1997,9(8):1735-1780.
[11]SCHUSTER M,PALIWAL K K.Bidirectional recurrent neural networks[J].IEEE Transactions on Signal Processing,1997,45(11):2673-2681.
[12]WU Y,SCHUSTER M,CHEN Z,et al.Google’s Neural Ma-chine Translation System:Bridging the Gap between Human and Machine Translation[J].arXiv:1609.08144.
[13]KIROS R,ZHU Y,SALAKHUTDINOV R R,et al.Skip-thought vectors[C]∥Advances in Neural Information Proces-sing Systems.2015:3294-3302.
[14]CHEN H,SUN M,TU C,et al.Neural Sentiment Classification with User and Product Attention[C]∥Conference on Empirical Methods in Natural Language Processing.2016:1650-1659.
[15]MAJUMDER N,PORIA S,GELBUKH A,et al.Deep learning-based document modeling for personality detection from text[J].IEEE Intelligent Systems,2017,32(2):74-79.
[16]ZHANG L,WANG S,LIU B.Deep learning for sentiment ana-lysis:A survey[J].arXiv:1801.07883.
[17]HU F,XU X,WANG J,et al.Memory-Enhanced Latent Semantic Model:Short Text Understanding for Sentiment Analysis[C]∥International Conference on Database Systems for Advanced Applications.Springer,Cham,2017:393-407.
[18]BACCIANELLA S,ESULI A,SEBASTIANI F.SentiWordNet 3.0:An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining[OL].http://nmis.isti.cnr.it/sebastiani/Publications/LREC10.pdf.
[19]PASCANU R,MIKOLOV T,BENGIO Y.On the difficulty of training recurrent neural networks[J].ICML (3),2013,28:1310-1318.
[20]GEOFFREY E H,NITISH S,ALEX K S,et al.Improving neural networks by preventing co-adaptation of feature detectors[J].arXiv:1207.0580.
[21]MCAULEY J J,LESKOVEC J.From amateurs to connoisseurs:modeling the evolution of user expertise through online reviews[C]∥Proceedings of the 22nd International Conference on World Wide Web.ACM,2013:897-908.
[22]CHRISTOPHER O.Understanding LSTM Networks[OL].http://colah.github.io/posts/2015-08-Understanding-LSTMs/.
[23]HU F,LI L,XU X,et al.Opinion extraction by distinguishing term dependencies and digging deep text features[J].Neural Computing & Applications,2018 (7):1-11.
[24]PASCANU R,MIKOLOV T,BENGIO Y.On the difficulty of training recurrent neural networks[C]∥International Conference on Machine Learning.2013,3:1310-1318.
[25]HE K M,ZHANG X,REN S,et al.Deep residual learning for image recognition[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778.
[26]KIM Y.Convolutional neural networks for sentence classification[C]∥EMNLP.2014:1746-1751.
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