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A Personalized Recommendation Algorithm based on Text Mining

Volume 14, Number 7, July 2018, pp. 1401-1410
DOI: 10.23940/ijpe.18.07.p3.14011410

Ningbin Zhang

School of Information Engineering, Xi’an University, Xian, 710062, China

(Submitted on March 29, 2018; Revised on May 3, 2018; Accepted on June 19, 2018)


The recommendation system is a new technology used to recommend products for customers from huge amounts of products by inferring objective users’ preferences based on their personal information or online behavior. This paper studied the main personalized recommendation technology for current e-commerce. It proposed a hybrid recommendation algorithm based on opinion mining. This system combines web data mining technology, i.e., takes advantage of user-generated content by mining customers’ online reviews. It is well known that online reviews can directly reflect a customer’s real emotions and expectations, so it is appropriate to extract a customer’s latent interest and preference from his/her reviews, thus refining recommendations and improving accuracy. Meanwhile, an experiment was conducted and the result demonstrated that our system could generate a reliable and realistic recommendation.


References: 15

          1. H. Huang, “Visual Analysis of Online Social Network”, Journal of the Chinese Academy of Sciences, vol.2, pp.229-237, 2015
          2. Y. M. Lin, X. L. Wang, T. Zhu, “Review of the Research on the Quality Control and Detection of User Reviews”, Journal of Software,vol.3, pp. 506-527,2014
          3. Z. Y. Li, “Study on The Utility Order Model of Online Commodity Review”, Modern Library and Information Technology, vol. 4, pp. 62-68,2013
          4. X. Q. Tan, S. He, “A Review of The Research on Music Personalized Recommendation System”, Modern Library and Information Technology, vol.9, pp.22-32, 2014
          5. X. B. Tang, Z. Zhang, “based on Mixed Graphs Online Social Network Personalized Recommendation System”, Information Theory and Practice, vol.2, pp. 91-95, 2013
          6. W. Wang, X. D. Huang, “Study on The Affective Recommendation System”, Information and Control, vol.2, pp. 218-228,2013
          7. W. Wang, H. W. Wang, Y. Meng, “Collaborative Filtering Recommendation Algorithm Research: Considering Online Review of Affective Tendency”, System Engineering Theory and Practice, vol.12, pp. 3238-3249, 2014
          8. H. S. Xia, “Online Reviews in The Preference Recognition Method of Commodity Attribute”, User information magazine, vol.9, pp. 197-201,2012
          9. W. Xiong, Y. J. Guo, “The Text Mining of Hotel Customer Online Reviews”, Journal of Beijing International Studies University, vol.11, pp. 38-47,2013
          10. M. Yang, W. Qi, X. B. Yan, “Analysis of Online Product Reviews the Utility”, Journal of Management Sciences,vol.5, pp. 65-75, 2012
          11. J. F. Zhong, “Based on The Emotion Lexis of Online Product Reviews Personalized Recommendation Method”, Zhengzhou University Journal (NATURAL SCIENCE EDITION), vol.2, pp. 48-51, 2011
          12. J. F. Zhong, “Fuzzy Intelligent Product Recommendation System based on Online Consumer Reviews”, System Engineering, vol.11, no.1, pp. 116-120, 2013
          13. L. Zhang, Z. X. Chen, “Analysis and Prediction of The Personality of Social Network Users”, Journal of Computer Science, vol.8, pp.1877-1894, 2014
          14. S. J. Zhao, “Based on Semantic and TF-IDF Project Similarity Calculation Method”, Computer Age, vol.5, pp. 1-6, 2015
          15. Y. J. Zhang, Y. L. Du, “Group Recommendation System and Its Application”, Journal of Computer Science, vol.4, pp.745-764, 2016


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