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IJMLC 2021 Vol.11(1): 12-20 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.1.1008

Learning Sentiment over Network Embedding for Recommendation System

Phatpicha Yochum, Liang Chang, Tianlong Gu, and Manli Zhu

Abstract—With the rapid development of Internet, various unstructured information, such as user-generated content, textual reviews, and implicit or explicit feedbacks have grown continuously. Though structured knowledge bases (KBs) which consist of a large number of triples exhibit great advantages in recommendation field recently. In this paper, we propose a novel approach to learn sentiment over network embedding for recommendation system based on the knowledge graph which we have been built, that is, we integrate the network embedding method with the sentiment of user reviews. Specifically, we use the typical network embedding method node2vec to embed the large-scale structured data into a low-dimensional vector space to capture the internal semantic information of users and attractions and apply the user weight scoring which is the combination of user review ratings and textual reviews to get similar attractions among users. Experimental results on real-world dataset verified the superior recommendation performance on precision, recall, and F-measure of our approach compared with state-of-the-art baselines.

Index Terms—Recommendation systems, review features, network embedding, collaborative filtering, knowledge graph.

Phatpicha Yochum, Liang Chang, Tianlong Gu, and Manli Zhu are with the Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004 P.R. China (e-mail: mink.phatpicha@gmail.com, changl@guet.edu.cn, cctlgu@guet.edu.cn, zhummanli@gmail.com).

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Cite: Phatpicha Yochum, Liang Chang, Tianlong Gu, and Manli Zhu, "Learning Sentiment over Network Embedding for Recommendation System," International Journal of Machine Learning and Computing vol. 11, no. 1, pp. 12-20, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

 

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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