Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
권호 표지
DOI QR코드

DOI QR Code

An Intelligent Recommendation System by Integrating the Attributes of Product and Customer in the Movie Reviews

영화 리뷰의 상품 속성과 고객 속성을 통합한 지능형 추천시스템

  • Hong, Taeho (College of Business Administration, Pusan National University) ;
  • Hong, Junwoo (College of Business Administration, Pusan National University) ;
  • Kim, Eunmi (Kookmin Information Technology Research Institute, Kookmin University) ;
  • Kim, Minsu (College of Business Administration, Pusan National University)
  • 홍태호 (부산대학교 경영학과) ;
  • 홍준우 (부산대학교 경영학과) ;
  • 김은미 (국민대학교 정보기술연구소) ;
  • 김민수 (부산대학교 경영학과)
  • Received : 2022.05.27
  • Accepted : 2022.06.17
  • Published : 2022.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

As digital technology converges into the e-commerce market across industries, online transactions have activated, and the use of online has increased. With the recent spread of infectious diseases such as COVID-19, this market flow is accelerating, and various product information can be provided to customers online. Providing a variety of information provides customers with various opportunities but causes difficulties in decision-making. The recommendation system can help customers to make a decision more effectively. However, the previous research on recommendation systems is limited to only quantitative data and does not reflect detailed factors of products and customers. In this study, we propose an intelligent recommendation system that quantifies the attributes of products and customers by applying text mining techniques to qualitative data based on online reviews and integrates the existing objective indicators of total star rating, sentiment, and emotion. The proposed integrated recommendation model showed superior performance to the overall rating-oriented recommendation model. It expects the new business value to be created through the recommendation result reflecting detailed factors of products and customers.

디지털 기술이 산업 전반의 전자상거래 시장에 융합되면서 온라인 거래의 활성화와 이용률을 증가시켰으며, 이러한 시장의 흐름은 최근 코로나와 같은 감염병이 확산함에 따라 더욱 가속화되어 다양한 상품 정보를 온라인을 통해 고객들에게 제공할 수 있게 되었다. 다양한 정보의 제공은 고객들에게 다양한 선택의 기회를 제공하지만 의사결정에 어려움을 주기도 한다. 추천시스템은 고객의 의사결정에 도움을 줄 수 있으나 기존 추천시스템 연구는 정량적 데이터만에 국한되어 있으며, 상품 및 고객의 세부적인 요인을 반영하지 못하였다. 이에 본 연구에서는 온라인 리뷰를 기반으로 정성적 데이터를 텍스트 마이닝 기법을 적용하여 상품 및 고객의 속성을 정량화하고 기존의 객관적 지표인 총평점과 감성 및 감정을 통합한 지능형 추천시스템을 제안한다. 제안된 지능형 추천모형은 총평점 위주의 추천 모형보다 우수한 추천성과를 보여주었으며, 상품 및 고객의 세부적 요소를 반영한 추천결과를 통해 새로운 비즈니스 가치를 창출할 것으로 기대한다.

Keywords

Acknowledgement

이 논문은 2019년 대한민국 교육부와 한국연구재단의 지원을 받아 수행된 연구임 (NRF-2019S1A5A2A03055790)

References

  1. 길호현. (2018). 텍스트마이닝을 위한 한국어 불용어 목록 연구. 우리말글, 78, 1-25. https://doi.org/10.18628/URIMAL.78..201809.1
  2. 김광수. (2000). 영화 선택 및 평가에 관한 연구. Korean Association for Advertising and Public Relations, 48, 139-164.
  3. 김민정, 김수현, 오지혜, 엄지윤, 강주영. (2021). SNS 텍스트 마이닝 기반 포스트 코로나 신트렌드 차박 여행 지도 제작 및 차박지 추천에 관한 연구. 한국IT서비스학회지, 20, 11-28. https://doi.org/10.9716/KITS.2021.20.1.011
  4. 김진화, 변현수, 이승훈. (2008). 온라인 리뷰와 미니멀리즘. 한국전자거래학회 심포지움 및 기타간행물, 235-252.
  5. 박대민. (2016). 뉴스 기사의 자연어처리:< 뉴스 소스 베타> 를 중심으로. 커뮤니케이션 이론, 12(1), 4-52.
  6. 박호연, 김경재. (2021). BERT 기반 감성분석을 이용한 추천시스템. 지능정보연구, 27(2), 1-15. https://doi.org/10.13088/JIIS.2021.27.2.001
  7. 윤호민, 최규완. (2020). 사용자 선호기반 개인화 음식메뉴 추천 기법 연구. 호텔경영학연구, 29(1), 83-100.
  8. 이세화. (2020). KETI 지능정보 플래그십 R&D 데이터.(주)아크릴, https://aihub.or.kr/opendata/keti-data/recognition-laguage/KETI-02-009
  9. 전병국, 안현철. (2015). 사용자 리뷰 마이닝을 결합한 협업 필터링 시스템: 스마트폰 앱 추천에의 응용. 지능정보연구, 21(2), 1-18. https://doi.org/10.13088/JIIS.2015.21.2.01
  10. 조신희, 이문용. (2014). 온라인 제품 리뷰의 유용성 결정 요인 분석을 통한 리뷰 활용 방안 도출. Entrue Journal of Information Technology, 13(1), 29-40.
  11. 현지연, 유상이, 이상용. (2019). 평점과 리뷰 텍스트 감성분석을 결합한 추천시스템 향상 방안 연구. 지능정보연구, 25(1), 219-239. https://doi.org/10.13088/JIIS.2019.25.1.219
  12. SKTBrain / KoBERT, https://github.com/SKTBRAIN/KOBERT
  13. Aggarwal, C. C., & Zhai, C. (2012). A survey of text classification algorithms. In Mining text data (pp. 163-222). Springer, Boston, MA.
  14. Al-maaitah, T. A., Majali, T. E., Alsoud, M., & Al- maaitah, D. A. (2021). The Impact of COVID-19 on the Electronic Commerce Users Behavior. Journal of Contemporary Issues in Business and Government, 27(1), 784-793.
  15. Anderson, E. W., & Sullivan, M. W. (1993). The antecedents and consequences of customer satisfaction for firms. Marketing science, 12(2), 125-143. https://doi.org/10.1287/mksc.12.2.125
  16. Basilico, J., & Hofmann, T. (2004, July). Unifying collaborative and content-based filtering. In Proceedings of the twenty-first international conference on Machine learning (p. 9).
  17. Breese, J. Heckerman, D., & Kadie, C. (1998). Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI.
  18. Cheng, Y. H., & Ho, H. Y. (2015). Social influence's impact on reader perceptions of online reviews. Journal of Business Research, 68(4), 883-887. https://doi.org/10.1016/j.jbusres.2014.11.046
  19. Darko, A. P., & Liang, D. (2022). Modeling customer satisfaction through online reviews: A FlowSort group decision model under probabilistic linguistic settings. Expert Systems with Applications, 195, 116649. https://doi.org/10.1016/j.eswa.2022.116649
  20. Dhar, S., & Bose, I. (2022). Walking on air or hopping mad? Understanding the impact of emotions, sentiments and reactions on ratings in online customer reviews of mobile apps. Decision Support Systems, 113769.
  21. Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), 61-70. https://doi.org/10.1145/138859.138867
  22. Hou, T., Yannou, B., Leroy, Y., & Poirson, E. (2019). Mining customer product reviews for product development: A summarization process. Expert Systems with Applications, 132, 141-150. https://doi.org/10.1016/j.eswa.2019.04.069
  23. Hu, N., Liu, L., & Zhang, J. J. (2008). Do online reviews affect product sales? The role of reviewer characteristics and temporal effects. Information Technology and management, 9(3), 201-214. https://doi.org/10.1007/s10799-008-0041-2
  24. Jeong, S. Y., & Kim, H. J. (2017). A recommender system using factorization machine. Journal of Digital Contents Society, 18(4), 707-712. https://doi.org/10.9728/DCS.2017.18.4.707
  25. Kiran, R., Kumar, P., & Bhasker, B. (2020). Oslcfit (organic simultaneous LSTM and CNN Fit): A novel deep learning based solution for sentiment polarity classification of reviews. Expert Systems with Applications, 157, 113488. https://doi.org/10.1016/j.eswa.2020.113488
  26. Lee, R. K., Chung, N., & Hong, T. (2019). Developing the online reviews based recommender models for multi-attributes using deep learning. The Journal of Information Systems, 28(1), 97-114. https://doi.org/10.5859/KAIS.2019.28.1.97
  27. Li, M., Huang, L., Tan, C. H., & Wei, K. K. (2013). Helpfulness of online product reviews as seen by consumers: Source and content features. International Journal of Electronic Commerce, 17(4), 101-136. https://doi.org/10.2753/jec1086-4415170404
  28. Liu, F., Lai, K.-H., Wu, J., & Duan, W. (2021). Listening to online reviews: A mixed-methods investigation of customer experience in the sharing economy. Decision Support Systems, 149, 113609. https://doi.org/10.1016/j.dss.2021.113609
  29. Ma, E., Cheng, M., & Hsiao, A. (2018). Sentiment analysis-a review and agenda for future research in hospitality contexts. International Journal of Contemporary Hospitality Management, 30(11), 3287-3308. https://doi.org/10.1108/IJCHM-10-2017-0704
  30. Moreo, A., Romero, M., Castro, J. L., & Zurita, J. M. (2012). Lexicon-based Comments-oriented News Sentiment Analyzer system. Expert Systems with Applications, 39(10), 9166-9180. https://doi.org/10.1016/j.eswa.2012.02.057
  31. Neelamegham, R., & Jain, D. (1999). Consumer choice process for experience goods: An econometric model and analysis. Journal of marketing research, 36(3), 373-386. https://doi.org/10.2307/3152083
  32. Park, D. H., Lee, J., & Han, I. (2007). The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement. International journal of electronic commerce, 11(4), 125-148. https://doi.org/10.2753/JEC1086-4415110405
  33. Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001, April). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web (pp. 285-295). arXiv:1301.7363.
  34. Singh, A., & Tucker, C. S. (2017). A machine learning approach to product review disambiguation based on function, form and behavior classification. Decision Support Systems, 97, 81-91. https://doi.org/10.1016/j.dss.2017.03.007
  35. Son, J., Kim, S. B., Kim, H., & Cho, S. (2015). Review and analysis of recommender systems. Journal of Korean institute of industrial engineers, 41(2), 185-208. https://doi.org/10.7232/JKIIE.2015.41.2.185
  36. Vany, A. D., & Walls, W. D. (1996). Bose-Einstein dynamics and adaptive contracting in the motion picture industry. The Economic Journal, 106(439), 1493-1514. https://doi.org/10.2307/2235197
  37. Vapnik, V. (1999). The nature of statistical learning theory. Springer science &business media.
  38. Xu, X. (2021). What are customers commenting on, and how is their satisfaction affected? Examining online reviews in the on-demand food service context. Decision Support Systems, 142, 113467. https://doi.org/10.1016/j.dss.2020.113467
  39. Yao, Z. Y., Park, Y. K., and Hong, T. H. (2020). A study on the Effect of Reviewer Attributes on the Usefulness of Online Reviews. Information Systems Journal, 29(2), 173-195.
  40. Yun, S. Y., & Yoon, S. D. (2020). Item-Based Collaborative Filtering Recommendation Technique Using Product Review Sentiment Analysis. Journal of the Korea Institute of Information and Communication Engineering, 24(8), 970-977. https://doi.org/10.6109/JKIICE.2020.24.8.970