Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
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Analysis of the Effects of E-commerce User Ratings and Review Helfulness on Performance Improvement of Product Recommender System

E-커머스 사용자의 평점과 리뷰 유용성이 상품 추천 시스템의 성능 향상에 미치는 영향 분석

  • FAN, LIU (Department of Business Administration, Graduate School, Kyung Hee University) ;
  • Lee, Byunghyun (Department of Big Data Analytics, Graduate School, Kyung Hee University) ;
  • Choi, Ilyoung (Graduate School of Business Administration, Kyung Hee University) ;
  • Jeong, Jaeho (Department of Business Administration, Graduate School, Kyung Hee University) ;
  • Kim, Jaekyeong (School of Management & Department of Big Data Analytics, Graduate School, Kyung Hee University)
  • ;
  • 이병현 (경희대학교 대학원 빅데이터응용학과) ;
  • 최일영 (경희대학교 경영대학원) ;
  • 정재호 (경희대학교 대학원 경영학과) ;
  • 김재경 (경희대학교 경영대학 & 대학원 빅데이터응용학과)
  • Received : 2022.02.27
  • Accepted : 2022.03.23
  • Published : 2022.03.31
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Abstract

Because of the spread of smartphones due to the development of information and communication technology, online shopping mall services can be used on computers and mobile devices. As a result, the number of users using the online shopping mall service increases rapidly, and the types of products traded are also growing. Therefore, to maximize profits, companies need to provide information that may interest users. To this end, the recommendation system presents necessary information or products to the user based on the user's past behavioral data or behavioral purchase records. Representative overseas companies that currently provide recommendation services include Netflix, Amazon, and YouTube. These companies support users' purchase decisions by recommending products to users using ratings, purchase records, and clickstream data that users give to the items. In addition, users refer to the ratings left by other users about the product before buying a product. Most users tend to provide ratings only to products they are satisfied with, and the higher the rating, the higher the purchase intention. And recently, e-commerce sites have provided users with the ability to vote on whether product reviews are helpful. Through this, the user makes a purchase decision by referring to reviews and ratings of products judged to be beneficial. Therefore, in this study, the correlation between the product rating and the helpful information of the review is identified. The valuable data of the evaluation is reflected in the recommendation system to check the recommendation performance. In addition, we want to compare the results of skipping all the ratings in the traditional collaborative filtering technique with the recommended performance results that reflect only the 4 and 5 ratings. For this purpose, electronic product data collected from Amazon was used in this study, and the experimental results confirmed a correlation between ratings and review usefulness information. In addition, as a result of comparing the recommendation performance by reflecting all the ratings and only the 4 and 5 points in the recommendation system, the recommendation performance of remembering only the 4 and 5 points in the recommendation system was higher. In addition, as a result of reflecting review usefulness information in the recommendation system, it was confirmed that the more valuable the review, the higher the recommendation performance. Therefore, these experimental results are expected to improve the performance of personalized recommendation services in the future and provide implications for e-commerce sites.

정보통신기술 발달로 스마트폰이 보급되면서, 온라인 쇼핑몰 서비스는 컴퓨터가 아닌 모바일로도 사용이 가능해졌다. 그로 인해 온라인 쇼핑몰 서비스를 이용하는 사용자는 급격히 증가하게 되고, 거래되는 제품의 종류 또한 방대해지고 있다. 따라서 기업은 이익을 최대화하기 위해서는 사용자가 관심을 가질만한 정보를 제공해주는 것이 중요하다. 이를 위해 사용자의 과거 행동 데이터나 행동 구매 기록을 기반으로 사용자에게 필요한 정보 또는 제품을 제시하는 것을 추천 시스템이라 한다. 현재 추천 서비스를 제공하는 대표적인 해외 기업으로는 Netflix, Amazon, YouTube 등이 있다. 최근 이러한 전자상거래 사이트에서는 사용자가 해당 제품에 대한 리뷰가 유용한지에 대해 투표할 수 있는 기능을 제공하고 있다. 이를 통해, 사용자는 유용하다고 판단되는 제품에 대한 리뷰와 평점을 참고하여 구매 의사결정을 내린다. 따라서 본 연구에서는 제품에 대한 평점과 리뷰의 유용성 정보 간의 상관관계를 파악하고, 리뷰의 유용성 정보를 추천 시스템에 반영하여 추천 성능을 확인하고자 한다. 또한 대부분의 사용자들은 만족한 제품에만 평점을 부여하는 경향이 있고 제품에 대한 평점이 높을수록 구매 의도가 높아지는 경향이 있다. 따라서 전통적인 협업 필터링 기법에 모든 평점을 반영한 결과와 4점과 5점 평점만을 반영한 추천 성능 결과를 비교하고자 한다. 이를 위해 본 연구에서는 Amazon에서 수집한 전자 제품 데이터를 사용하였으며, 실험 결과는 평점과 리뷰 유용성 정보 간 상관관계가 있는 것으로 확인되었다. 또한 모든 평점과 4점과 5점 평점만을 추천 시스템에 반영하여 추천 성능을 비교한 결과, 4점과 5점 평점만을 추천 시스템에 반영한 결과의 추천 성능이 더 높게 나타났다. 그리고 리뷰 유용성 정보를 추천 시스템에 반영한 결과는 리뷰가 유용할수록 추천 성능은 높게 나타나는 것으로 확인하였다. 따라서 이러한 실험 결과는 향후 개인화 추천 서비스의 성능 향상에 기여하고, 전자상거래 사이트에 시사점을 제공할 수 있을 것으로 본다.

Keywords

Acknowledgement

이 논문 또는 저서는 2020년 대한민국 교육부와 한국연구재단의 지원을 받아 수행된 연구임(NRF-2020S1A5B8103855)

References

  1. Abdollahi, B., and O. Nasraoui, "Using explainability for constrained matrix factorization," In Proceedings of the Eleventh ACM Conference on Recommender Systems, (2017), 79-83.
  2. Al-Bashiri, H., M. A. Abdulgabber, A. Romli, and H. Kahtan, "An improved memory-based collaborative filtering method based on the TOPSIS technique," PloS one, Vol.13, No.10 (2018), e0204434. https://doi.org/10.1371/journal.pone.0204434
  3. Al-Smadi, M., B. Talafha, M. Al-Ayyoub, and Y. Jararweh, "Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews," International Journal of Machine Learning and Cybernetics, Vol.10, No.8(2019), 2163-2175. https://doi.org/10.1007/s13042-018-0799-4
  4. Bang, H., H. Lee, and J. H. Lee, "TV Program recommender system using viewing time patterns," Journal of the Korean Institute of Intelligent Systems, Vol.25, No.5(2015), 431-436. https://doi.org/10.5391/JKIIS.2015.25.5.431
  5. Castelli, M., L. Manzoni, L. Vanneschi, and A. Popovic, "An expert system for extracting knowledge from customers' reviews: The case of amazon. com, inc.," Expert Systems with Applications, Vol.84, 117-126. https://doi.org/10.1016/j.eswa.2017.05.008
  6. Chintagunta, P. K., S. Gopinath, and S. Venkataraman, "The effects of online user reviews on movie box office performance: Accounting for sequential rollout and aggregation across local markets," Marketing science, Vol.29, No.5(2010), 944-957. https://doi.org/10.1287/mksc.1100.0572
  7. Choeh, J. Y., S. K. Lee, and Y. B. Cho, "Applying rating score's reliability of customers to enhance prediction accuracy in recommender system," The Journal of the Korea Contents Association, Vol.13, No.7(2013), 379-385. https://doi.org/10.5392/JKCA.2013.13.07.379
  8. Eslami, S. P., M. Ghasemaghaei, and K. Hassanein, "Which online reviews do consumers find most helpful? A multi-method investigation," Decision Support Systems, Vol.113, (2018) 32-42. https://doi.org/10.1016/j.dss.2018.06.012
  9. Fang, Q., C. Xu, M. S. Hossain, and G. Muhammad, "Stcaplrs: A spatial-temporal context-aware personalized location recommendation system," ACM Transactions on Intelligent systems and technology (TIST), Vol.7, No.4(2016), 1-30.
  10. Ge, S., T. Qi, C. Wu, F. Wu, X. Xie, and Y. Huang, "Helpfulness-aware review based neural recommendation," CCF transactions on pervasive computing and interaction, Vol.1, No.4(2019), 285-295. https://doi.org/10.1007/s42486-019-00023-0
  11. Goldberg, L. R., "The development of markers for the Big-Five factor structure," Psychological assessment, Vol.4, No.1(1992), 26. https://doi.org/10.1037/1040-3590.4.1.26
  12. Gomez-Uribe, C. A., and N. Hunt, "The netflix recommender system: Algorithms, business value, and innovation," ACM Transactions on Management Information Systems (TMIS), Vol.6, No.4(2015), 1-19.
  13. Koohi, H., & K. Kiani, "User based collaborative filtering using fuzzy C-means," Measurement, 91, (2016), 134-139. https://doi.org/10.1016/j.measurement.2016.05.058
  14. Lee, S. H., A. R. Jo, and H. Y. Lee, "The Medical Service Customers Satisfaction Factors Extracted from Online Hospital Review Data Using Latent DirichletAllocation Method," Journal of Korea Service Management Society, Vol.18, No.5(2017), 23-44. https://doi.org/10.15706/jksms.2017.18.5.002
  15. Linden, G., B. Smith, and J. York, "Amazon. com recommendations: Item-to-item collaborative filtering," IEEE Internet computing, Vol.7, No.1(2003), 76-80. https://doi.org/10.1109/MIC.2003.1167344
  16. Lu, L., W. Xu, and S. Qiao, "A fast SVD for multilevel block Hankel matrices with minimal memory storage," Numerical Algorithms, Vol.69, No.4(2015), 875-891. https://doi.org/10.1007/s11075-014-9930-0
  17. Moore, S. G., "Attitude predictability and helpfulness in online reviews: The role of explained actions and reactions," Journal of Consumer Research, Vol.42, No.1(2015), 30-44. https://doi.org/10.1093/jcr/ucv003
  18. Nakayama, M., and Y. Wan, "The cultural impact on social commerce: A sentiment analysis on Yelp ethnic restaurant reviews," Information & Management, Vol.56, No.2(2019), 271-279. https://doi.org/10.1016/j.im.2018.09.004
  19. Pradel, B., N. Usunier, and P. Gallinari, "Ranking with non-random missing ratings: influence of popularity and positivity on evaluation metrics," In Proceedings of the sixth ACM conference on Recommender systems, (2012), 147-154
  20. Qiu, J., J. Wang, S. Yao, K. Guo, B. Li, E. Zhou, and H. Yang, "Going deeper with embedded fpga platform for convolutional neural network," In Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, (2016), 26-35.
  21. Qiu, J., Q. Wu, G. Ding, Y. Xu, and S. Feng, "A survey of machine learning for big data processing," EURASIP Journal on Advances in Signal Processing, Vol.2016, No.1(2016), 1-16. https://doi.org/10.1186/s13634-015-0293-z
  22. Ren, G., and T. Hong, "Examining the relationship between specific negative emotions and the perceived helpfulness of online reviews," Information Processing & Management, Vol.56, No.4(2019), 1425-1438. https://doi.org/10.1016/j.ipm.2018.04.003
  23. Shani, G., and A. Gunawardana, "Evaluating recommendation systems," In Recommender systems handbook, Springer, Boston, 2011.
  24. Shengli, L., and L. Fan, "The interaction effects of online reviews and free samples on consumers' downloads: An empirical analysis," Information Processing & Management, Vol.56, No.6(2019), 102071. https://doi.org/10.1016/j.ipm.2019.102071
  25. Smith, B., and G. Linden, "Two decades of recommender systems at Amazon. com," Ieee internet computing, Vol.21, No.3(2017), 12-18. https://doi.org/10.1109/MIC.2017.72
  26. Srifi, M., A. Oussous, A. Ait Lahcen, and S. Mouline, "Recommender systems based on collaborative filtering using review texts?A survey," Information, Vol.11, No.6(2020), 317. https://doi.org/10.3390/info11060317
  27. Su, X., and T. M. Khoshgoftaar, "A survey of collaborative filtering techniques," Advances in artificial intelligence, (2009), 2009.
  28. Sun, Y., Z. Wang, P. Fu, Q. Jiang, T. Yang, J. Li, and X. Ge, "The impact of relative humidity on aerosol composition and evolution processes during wintertime in Beijing, China," Atmospheric Environment, Vol.77, (2013), 927-934. https://doi.org/10.1016/j.atmosenv.2013.06.019
  29. Thakkar, P., K. Varma, V. Ukani, S. Mankad, and S. Tanwar, "Combining user-based and item-based collaborative filtering using machine learning," In Information and Communication Technology for Intelligent Systems, Springer, Singapore, 2019.
  30. Wang, Y., J. Wang, and T. Yao, "What makes a helpful online review? A meta-analysis of review characteristics," Electronic Commerce Research, Vol.19, No.2(2019), 257-284. https://doi.org/10.1007/s10660-018-9310-2
  31. Yun, Y., D. Hooshyar, J. Jo, and H. Lim, "Developing a hybrid collaborative filtering recommendation system with opinion mining on purchase review," Journal of Information Science, Vol.44 No.3(2018), 331-344. https://doi.org/10.1177/0165551517692955
  32. Zarzour, H., Z. Al-Sharif, M. Al-Ayyoub, and Y. Jararweh, "A new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques," In 2018 9th international conference on information and communication systems (ICICS), (2018), 102-106.