Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

Item-Based Collaborative Filtering Recommendation Technique Using Product Review Sentiment Analysis

상품 리뷰 감성분석을 이용한 아이템 기반 협업 필터링 추천 기법

  • Yun, So-Young (Information & Computer Center, Pukyong National University) ;
  • Yoon, Sung-Dae (Department of Computer Engineering, Pukyong National University)
  • Received : 2020.06.20
  • Accepted : 2020.06.24
  • Published : 2020.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

The collaborative filtering recommendation technique has been the most widely used since the beginning of e-commerce companies introducing the recommendation system. As the online purchase of products or contents became an ordinary thing, however, recommendation simply applying purchasers' ratings led to the problem of low accuracy in recommendation. To improve the accuracy of recommendation, in this paper suggests the method of collaborative filtering that analyses product reviews and uses them as a weighted value. The proposed method refines product reviews with text mining to extract features and conducts sentiment analysis to draw a sentiment score. In order to recommend better items to user, sentiment weight is used to calculate the predicted values. The experiment results show that higher accuracy can be gained in the proposed method than the traditional collaborative filtering.

협업 필터링 추천 기법은 전자상거래 기업들이 추천시스템을 도입한 이래로 가장 널리 사용되고 있다. 그러나 온라인에서 상품이나 콘텐츠의 구매가 일상화되면서 단순히 구매 고객의 평점만을 사용하는 추천 방식으로는 추천의 정확성이 낮아지는 문제점이 발생하였다. 본 논문에서는 추천의 정확성을 향상시키기 위해, 상품 리뷰를 분석하고 이를 가중치로 사용한 협업 필터링 추천 기법을 제안한다. 제안하는 기법은 상품에 대한 리뷰를 텍스트 마이닝 기법으로 정제하여 특징을 추출하고 감성 기반 분석을 통해 감성 점수를 산출한다. 사용자에게 더 나은 아이템을 추천하기 위해 산출된 점수를 아이템 예측 값 계산 시 가중치로 사용한다. 실험을 통해 전통적인 협업 필터링 기법보다 제안하는 기법의 정확성이 향상되는 것을 확인할 수 있었다.

Keywords

References

  1. J. Sun, Y. Zhai, Y. Zhao, J. Li, and N. Yan, "Information Acquisition and Analysis Technology of Personalized Recommendation System Based on Case-Based Reasoning for Internet of Things," 2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), Zhengzhou, CHINA, pp. 107-110, 2018.
  2. S. Zhang, L. Yao, A. Sun, and Y. Tay, "Deep Learning based Recommender System: A Survey and New Perspectives," ACM Computing Surveys, vol. 52, no. 1, Article 5, pp. 1-38, Feb. 2019.
  3. J. Lu, D. Wu, M. Mao, W. Wang, and G. Zhang, "Recommender system application developments: A survey," Decision Support Systems, vol. 74, pp. 12-31, Jun. 2015. https://doi.org/10.1016/j.dss.2015.03.008
  4. D. Kluver, M. D. Ekstrand, and J. A. Konstan, "Rating-Based Collaborative Filtering: Algorithms and Evaluation," Social Information Access, pp. 344-390, May. 2018.
  5. N. Archak, A. Ghose, and P. G.Ipeirotis, "Deriving the Pricing Power of Product Feature by Mining Consumer Reviews," Management Science, vol. 57, no. 8, pp. 1485-1509, Jun. 2011. https://doi.org/10.1287/mnsc.1110.1370
  6. J. Lee, "How eWOM Reduces Uncertainties in Decisionmaking Process : Using the Concept of Entropy in Information Theory," The Journal of Society for e-Business Studies, vol. 16, no. 4, pp. 241-256, Nov. 2011.
  7. F. Li, N. Liu, K. Zhao, Q. Yang, and X. Zhu, "Incorporating Reviewer and Product Information for Review Rating Prediction," Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Catalonia, pp. 1820-1825, 2011.
  8. L. Jiang, Y. Cheng, L. Yang, J. Li, H. Yan, and X. Wang, "A trust-based collaborative filtering algorithm for E-commerce recommendation system," Journal of Ambient Intelligence and Humanized Computing, vol. 10, pp. 3023-3034, Jun. 2018.
  9. D. Li, C. Chen, Q. Lv, L. Shang, Y. Zhao, T. Lu, and N. Gu, "An algorithm for efficient privacy-preserving item-based collaborative filtering," Future Generation Computer Systems, vol. 55, pp. 311-320, Feb. 2016. https://doi.org/10.1016/j.future.2014.11.003
  10. R. Feldman, and J. Sanger, The Text Mining Handbook : Advanced Approaches in Analyzing Unstructured Data, Cambridge. United Kingdom, Cambridge University Press, 2007.
  11. C. Holton, "Identifying disgruntled employee systems fraud risk through text mining: A simple solution for a multi-billion dollar problem," Decision Support Systems, vol. 46, no. 4, pp. 853-864, Mar. 2009. https://doi.org/10.1016/j.dss.2008.11.013
  12. B. Pang, and L. Lee, "Opinion Mining and Sentiment Analysis," Foundations and Trends in Information Retrieval, vol. 2, no. 1-2, pp. 1-135, Jan. 2008. https://doi.org/10.1561/1500000011
  13. S. Elbagir, and J. Yang, "Twitter Sentiment Analysis Using Natural Language Toolkit and VADER Sentiment," Proceedings of the International MultiConference of Engineers and Computer Scientists 2019, Hong Kong, pp. 216-225, 2019.
  14. R. Mu, and X. Zeng, "Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph," Hindawi Mathematical Problems in Engineering, vol. 2018, pp. 1-11, Jul. 2018.