Home > Archive > 2019 > Volume 9 Number 4 (Aug. 2019) >
IJMLC 2019 Vol.9(4): 425-431 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.4.821

Contextual Sentiment Based Recommender System to Provide Recommendation in the Electronic Products Domain

N. A. Osman, S. A. M. Noah, and M. Darwich

Abstract—The rush to purchase the latest products sometimes prevents people from thinking things through completely. Consequently, recommender services are increasingly emerging. By looking at industry trends, interviewing dozens of leading industry stakeholders, and using publicly available information, it is important to filter out the most relevant information for consumer electronics before purchasing their items. This paper presents an electronic product recommender system based on contextual information from sentiment analysis. The recommendation algorithms mostly rely on users’ rating to make prediction of items. Such ratings are usually insufficient and very limited. We present a contextual information sentiment based model for recommender system by making use of user comments and preferences to provide a recommendation. The purpose of this approach is to avoid term ambiguity which is so called domain sensitivity problem in recommendation. The proposed contextual information sentiment-based model illustrates better performance by using results of RMSE and MAE measurements as compared to the conventional collaborative filtering approach in electronic product recommendation.

Index Terms—Collaborative filtering, recommender systems, sentiment analysis, electronic product, domain sensitivity.

The authors are with the Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia (e-mail: n.aidaosman@gmail.com, shahrul@ukm.edu.my, modarwish@hotmail.com).


Cite: N. A. Osman, S. A. M. Noah, and M. Darwich, "Contextual Sentiment Based Recommender System to Provide Recommendation in the Electronic Products Domain," International Journal of Machine Learning and Computing vol. 9, no. 4, pp. 425-431, 2019.

Copyright © 2019 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

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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