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논문 상세정보

검색 키워드를 활용한 하이브리드 협업필터링 기반 상품 추천 시스템

A Hybrid Collaborative Filtering-based Product Recommender System using Search Keywords

초록

추천시스템(recommender system)은 고객의 선호도를 예측하여 상품과 서비스를 제공하는 기법으로, 현재 다양한 온라인 서비스에 활용되고 있다. 이와 관련된 많은 선행 연구들은 협업필터링(collaborative filtering)에 기반한 추천시스템을 제안하였는데, 대부분의 경우 고객의 구매 내역 또는 평점 데이터만 사용하여 진행되었다. 오늘날 소비자들은 제품을 구매하는 과정에서 온라인 검색 행동을 하여 관심있는 제품을 찾는다. 그렇기 때문에 검색 키워드 데이터는 고객의 선호도를 파악하는데 매우 유용한 정보일 수 있다. 그러나 지금까지 추천시스템 연구에서 사용되는 경우는 거의 없었다. 이에 본 연구는 고객의 검색 행동에 주목하여 온라인 쇼핑몰 고객의 검색 키워드 데이터와 구매 데이터를 고려한 하이브리드 협업 필터링을 제안하였다. 본 연구는 제안된 모델의 적용 가능성을 검증하기 위해 실제 온라인 쇼핑몰 데이터를 사용하여 성능을 검증하였다. 연구 결과, 추천 상품의 개수가 많아질수록 고객의 검색 키워드를 기반으로 구축된 협업필터링의 추천 성능이 향상되는 반면 일반적인 협업필터링의 성능은 추천된 상품의 개수가 많아질수록 점차 감소함을 발견하였다. 따라서 본 연구는 검색 키워드 데이터를 활용한 하이브리드 협업필터링이 고객의 선호도를 반영한 추천할 수 있으며, 구매이력 데이터의 정보부족을 해결할 수 있음을 확인하였다. 이는 기존의 정량 데이터만을 활용한 추천 시스템이 아닌, 비정형 데이터인 텍스트를 사용함으로써 새로운 하이브리드 협업필터링 구축 방법을 제안했다는 점에서 의의가 있다.

Abstract

A recommender system is a system that recommends products or services that best meet the preferences of each customer using statistical or machine learning techniques. Collaborative filtering (CF) is the most commonly used algorithm for implementing recommender systems. However, in most cases, it only uses purchase history or customer ratings, even though customers provide numerous other data that are available. E-commerce customers frequently use a search function to find the products in which they are interested among the vast array of products offered. Such search keyword data may be a very useful information source for modeling customer preferences. However, it is rarely used as a source of information for recommendation systems. In this paper, we propose a novel hybrid CF model based on the Doc2Vec algorithm using search keywords and purchase history data of online shopping mall customers. To validate the applicability of the proposed model, we empirically tested its performance using real-world online shopping mall data from Korea. As the number of recommended products increases, the recommendation performance of the proposed CF (or, hybrid CF based on the customer's search keywords) is improved. On the other hand, the performance of a conventional CF gradually decreased as the number of recommended products increased. As a result, we found that using search keyword data effectively represents customer preferences and might contribute to an improvement in conventional CF recommender systems.

저자의 다른 논문

참고문헌 (35)

  1. 1. Adomavicius, G., and A. Tuzhilin, "Context-aware recommender systems," In Recommender Systems Handbook, Springer, Boston, MA, 2011 
  2. 2. Balabanovic, M., and Y. Shoham, "Fab: content-based, collaborative recommendation," Communications of the ACM, Vol.40, No.3 (1997), 66-72. 
  3. 3. Billsus, D., and M. J. Pazzani, "Learning Collaborative Information Filters," In International Conference of Machine Learning, Vol. 98, (1998), 46-54. 
  4. 4. Breese, J. S., D. Heckerman, and C. Kadie, "Empirical analysis of predictive algorithms for collaborative filtering," Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, (1998), 43-52. 
  5. 5. Cho S.-e., and H.-s. Lim, "A Study on Product Recommendation System Based on User Search keyword," Journal of Digital Contents Society, Vol.20, No.2(2019), 315-320. 
  6. 6. Cho, S.-y., J.-e. Choi, K.-h. Lee, and H.-w. Kim, "An Online Review Mining Approach to a Recommendation System," Information Systems Review, Vol.17, No.3(2015), 95-111. 
  7. 7. Cho, Y. H., J. K. Kim, D. H. Ahn, and H. A. Lee, "An Explanation based Recommender System using Collaborative Filtering: WebCF-Exp," Korean Management Review, Vol.35, No.2 (2006), 493-19. 
  8. 8. Choi D.-j., J.-y. Park, S.-b. Park, J.-t. Lim, J.-o Song, K.-s. Bok, and J.-s. Yoo, "Personalized Recommendation Considering Item Confidence in E-Commerce," Journal of the Korea Contents Association, Vol.19, No.3(2019), 171-182. 
  9. 9. Choi, S. B. and H. C. Ahn, "A study on the improvement of collaborative filtering prediction accuracy using online user review sentiment analysis," In Proceedings of the Korea Intelligent Information System Society Conference, (2017), 30-31. 
  10. 10. Kang, E. J. and Y. S. Choi, "Influence of information retrieval using A.I speaker on online purchasing experience - based on AISAS model," In Proceedings of HCI Korea 2019, (2019), 425-430 
  11. 11. Karvelis, P., D., Gavrilis, G., Georgoulas, and C. Stylios, "Topic recommendation using Doc2Vec," In 2018 IEEE International Joint Conference on Neural Networks (IJCNN), (2018), 1-6. 
  12. 12. Ki, H., J.-h. Lee, H.-w. Park, M.-j. Chae, S.-w. Choi and J. Park, "Inferring User Traits from Applications Installed on a Smart Phone," Journal of KIISE: Software and Applications, Vol.45, No.12(2018), 1240-1249. 
  13. 13. Jeong, J., M. Jee, M. Go, H. Kim, H. Lim, Y. Lee, and W. Kim, "Related Documents Classification System by Similarity between Documents," Journal of Broadcast Engineering, Vol.24, No.1(2019), 77-86. 
  14. 14. Jeong J.-w., W.-s. Hwang, H.-j. Lee, S.-w. Kim, "Recommendation Exploiting Search-Keywords in Online Shopping," Proceedings on KIISE Conference. Vol. 39. No. 2(2012), 95-97 
  15. 15. Kim, H., S. Chae, J. Yoo, and S. Bae, "Online shopping mall customer purchasing type clustering and purchasing power evaluation model," Proceedings on Korean Institute of Industrial Engineers, 2019, 2597-2616 
  16. 16. Kim, K. S., "A hybrid collaborative filtering algorithm for personalized recommendations and its application to the internet electronic commerce," The Journal of Internet Electronic Commerce Research, Vol.8, No.4(2008), 1-20. 
  17. 17. Kim, M. G. and K.-j. Kim, "Recommender Systems using Structural Hole and Collaborative Filtering," Journal of Intelligence and Information Systems, Vol.20, No.4(2014), 107-120. 
  18. 18. Kim, M., N. G. Kim, and I. H. Jung, "A Methodology for Extracting Shopping-Related Keywords by Analyzing Internet Navigation Patterns," Journal of Intelligence and Information Systems, Vol.20, No.2(2014), 123-136. 
  19. 19. Ku, M. J. and H. C. Ahn, "A Hybrid Recommender System based on Collaborative Filtering with Selective Use of Overall and Multicriteria Ratings," Journal of Intelligence and Information Systems, Vol.24, No.2(2018), 85-109. 
  20. 20. Le, Q. and T., Mikolov, "Distributed representations of sentences and documents," In Proceedings of the International Conference on Machine Learning, (2014), 1188-1196. 
  21. 21. Lee, H. S. and P. S. Kim, "The Effect of Consumer's Technology Acceptance and Resistance on Intention to Use of Artificial Intelligence (AI)," Korean Management Review, Vol.48, No.5(2019), 1195-1219 
  22. 22. Lee, R. K., N. H. Chung, and T. H. Hong, "Developing the online reviews based recommender models for multi-attributes using deep learning," Journal of Information Systems, Vol.28, No.1(2019), 97-114. 
  23. 23. Lee Y. S., K. C. Cha, and S.-h. Kim. "Internet Search Behavior and Box Office Performance," Korean Management Review, Vol.45, No.5 (2016), 1501-1526. 
  24. 24. Mikolov, T., I. Sutskever, K. Chen, G. Corrado, and J. Dean, "Distributed representations of words and phrases and their compositionality," Advances in Neural Information Processing Systems, (2013), 3111-3119. 
  25. 25. Nandi, R. N., M. A., Zaman, T., Al Muntasir, S. H., Sumit, T., Sourov, and M. J. U. Rahman, "Bangla news recommendation using doc2vec," In 2018 International Conference on Bangla Speech and Language Processing (ICBSLP), (2018), 1-5. 
  26. 26. Park, J., Y. H. Cho, and J. K. Kim, "Social Network : A Novel Approach to New Customer Recommendations," Journal of Intelligence and Information Systems, Vol.15, No.1(2009), 123-140. 
  27. 27. Park, J., and N. Kim, "Multi-Vector Document Embedding Using Semantic Decomposition of Complex Documents," Journal of Intelligence and Information Systems, Vol.25, No.3 (2019), 19-41. 
  28. 28. Phi, V. T., L., Chen, and Y. Hirate, "Distributed representation based recommender systems in e-commerce," In DEIM Forum, (2016). 
  29. 29. Sarwar, B., G., Karypis, J. A. Konstan, and J. Riedl, "Analysis of Recommendation Algorithms for E-commerce," Proceedings of ACM E-commerce 2000 Conference, (2000), 158-167. 
  30. 30. Shim, J., H. R. Won, and H. Ahn, "Doc2vec-based intelligent fake news classification model using domestic news articles," Proceedings of the Korea Intelligent Information System Society Conference, 1-3. 
  31. 31. Shin J. H., J. H. Song, K. S. Bok and J. S. Yoo, "Personalized Travel Destination Recommendation Scheme through Hybrid Collaborative Filtering," Proceedings of the Korea Contents Association, (2018), 383-384. 
  32. 32. Stiebellehner, S., J., Wang, and S., Yuan, "Learning Continuous User Representations through Hybrid Filtering with doc2vec," arXiv preprint arXiv 1801.00215., (2017). 
  33. 33. Takacs, G., I., Pilaszy, B. Nemeth, and D., Tikk, "Scalable collaborative filtering approaches for large recommender systems." Journal of Machine Learning Research, (2009), 623-656. 
  34. 34. Wu, C., and M. Yan, "Session-aware information embedding for e-commerce product recommendation," In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, (2017), 2379-2382. 
  35. 35. Zhang, X., J., Liu, B., Cao, Q., Xiao, and Y. Wen, "Web Service Recommendation via Combining Doc2Vec-Based Functionality Clustering and DeepFM-Based Score Prediction," In 2018 IEEE International Conference on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom), (2018), 509-516. 

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