Knowledge Management Research (지식경영연구)

The Knowledge Management Society of Korea (한국지식경영학회)
권호 표지
DOI QR코드

DOI QR Code

Application of Domain Knowledge in Transaction-based Recommender Systems through Word Embedding

트랜잭션 기반 추천 시스템에서 워드 임베딩을 통한 도메인 지식 반영

  • 최영제 (연세대학교 산업공학대학원) ;
  • 문현실 (국민대학교 경영대학원) ;
  • 조윤호 (국민대학교 경영학부 빅데이터경영통계전공)
  • Received : 2020.01.19
  • Accepted : 2020.02.26
  • Published : 2020.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

In the studies for the recommender systems which solve the information overload problem of users, the use of transactional data has been continuously tried. Especially, because the firms can easily obtain transactional data along with the development of IoT technologies, transaction-based recommender systems are recently used in various areas. However, the use of transactional data has limitations that it is hard to reflect domain knowledge and they do not directly show user preferences for individual items. Therefore, in this study, we propose a method applying the word embedding in the transaction-based recommender system to reflect preference differences among users and domain knowledge. Our approach is based on SAR, which shows high performance in the recommender systems, and we improved its components by using FastText, one of the word embedding techniques. Experimental results show that the reflection of domain knowledge and preference difference has a significant effect on the performance of recommender systems. Therefore, we expect our study to contribute to the improvement of the transaction-based recommender systems and to suggest the expansion of data used in the recommender system.

Keywords

References

  1. 김민정, 조윤호 2015. "빅데이터 기반 추천 시스템 구현을 위한 다중 프로파일 앙상블 기법," 지능정보연구 (21:4), pp. 93-110. https://doi.org/10.13088/jiis.2015.21.4.093
  2. 박현정, 송민채, 신경식 2018. "CNN을 적용한 한국어 상품평 감성 분석: 형태소 임베딩을 중심으로," 지능정보연구 (24:2), pp. 59-83. https://doi.org/10.13088/JIIS.2018.24.2.059
  3. 심재문, 강지욱, 권오병 2010. "상황인식 기술을 이용한 운전자 선호도 기반 교통상세정보 추천 시스템," 지식경영연구 (11:2), pp. 75-93.
  4. 이재성, 김재경, 강병욱 2019. "RFM 다차원 분석 기법을 활용한 암시적 사용자 피드백 기반 협업 필터링 개선 연구," 지능정보연구 (25:1), pp. 139-161. https://doi.org/10.13088/jiis.2019.25.1.139
  5. 이재영, 고혜민 2015. "온라인 추천정보와 선호 유사성의 역할: 2단계 구매 의사 결정 모델을 중심으로," 지식경영연구 (16:3), pp. 149-169. https://doi.org/10.15813/kmr.2015.16.3.008
  6. 정영진, 조윤호 2017. "온라인 구매 행태를 고려한 토픽 모델링 기반 도서 추천," 지식경영연구 (18:4), pp. 97-118 https://doi.org/10.15813/kmr.2017.18.4.004
  7. 조현수, 이상구 2017. "FastText를 적용한 한국어 단어 임베딩," 한국정보과학회 2017 한국소프트웨어종합학술대회 논문집, pp. 705-707.
  8. Barkan, O., and N. Koenigstein. 2016. "Item2vec: neural item embedding for collaborative filtering," In 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing, pp. 1-6.
  9. Bojanowski, P., Grave, E., Joulin, A. and Mikolov, T. 2017. "Enriching word vectors with subword information," Transactions of the Association for Computational Linguistics (5), pp. 135-146. https://doi.org/10.1162/tacl_a_00051
  10. Breese, J.S., Heckerman, D., Kadie, C. 1998. "Empirical analysis of predictive algorithms for collaborative filtering," In Proceedings of the 14th Annual Conference on Uncertainty in Artificial Intelligence, pp. 43-52.
  11. Brusilovsky, P. and He, D. 2018. "Social information access : systems and technologies,", Springer
  12. Gunawardana, A. and Shani, G. 2009. "A survey of accuracy evaluation metrics of recommendation tasks," Journal of Machine Learning Research (10), pp. 2935-2962.
  13. Guo, G., Qiu, H., Tan, Z., Liu, Y., Ma, J. and Wang, X. 2017. "Resolving data sparsity by multi-type auxiliary implicit feedback for recommender systems," Knowledge-Based Systems (138), pp. 202-207. https://doi.org/10.1016/j.knosys.2017.10.005
  14. Haucap, J. and Heimeshoff, U. 2014. "Google, Facebook, Amazon, eBay: Is the Internet driving competition or market monopolization?," International Economics and Economic Policy (11:1), pp. 49-61. https://doi.org/10.1007/s10368-013-0247-6
  15. Harper, F.M. and Konstan, J.A., 2015. " The movielens datasets: History and context," Acm transactions on interactive intelligent systems, (5:4), pp. 1-19. https://doi.org/10.1145/2827872
  16. Herlocker, J. L., Konstan, J. A., Terveen, L. G., and Riedl, J. T. 2004. "Evaluating collaborative filtering recommender systems," ACM Transactions on Information Systems (22:1), pp. 5-53. https://doi.org/10.1145/963770.963772
  17. He, X., Liao, L., Zhang, H., Nie, L., Hu, X. and Chua, T.S. 2017. "Neural collaborative filtering," In Proceedings of the 26th international conference on world wide web, pp. 173-182.
  18. He, X., Zhang, H., Kan, M.Y. and Chua, T.S. 2016. "Fast matrix factorization for online recommendation with implicit feedback," In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 549-558.
  19. Hsu, C.N., Chung, H.H. and Huang, H.S. 2004. "Mining skewed and sparse transaction data for personalized shopping recommendation," Machine Learning (57:1-2), pp. 35-59. https://doi.org/10.1023/B:MACH.0000035471.28235.6d
  20. Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J. 1997. "GroupLens: applying collaborative filtering to usenet news," Communications of the ACM (40:3), pp. 77-87. https://doi.org/10.1145/245108.245126
  21. Koren, Y. 2008. "Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model," In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426-434.
  22. Lathia, N., Hailes, S., Capra, L., and Amatriain, X. 2010. "Temporal diversity in recommender systems," In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pp. 210-217.
  23. Lee, S.K., Cho, Y.H. and Kim, S.H. 2010. "Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations," Information Sciences (180:11), pp. 2142-2155. https://doi.org/10.1016/j.ins.2010.02.004
  24. Lee, T.Q., Park, Y. and Park, Y.T. 2008. "A timebased approach to effective recommender systems using implicit feedback," Expert systems with applications (34:4), pp. 3055-3062. https://doi.org/10.1016/j.eswa.2007.06.031
  25. Lee, T.Q., Park, Y. and Park, Y.T. 2009. "An empirical study on effectiveness of temporal information as implicit ratings," Expert systems with Applications (36:2), pp. 1315-1321. https://doi.org/10.1016/j.eswa.2007.11.047
  26. Levy, O. and Goldberg, Y. 2014. "Neural word embedding as implicit matrix factorization," In Advances in neural information processing systems, pp. 2177-2185.
  27. Liu, M., Pan, W., Liu, M., Chen, Y., Peng, X. and Ming, Z. 2017. "Mixed similarity learning for recommendation with implicit feedback," Knowledge-Based Systems (119), pp. 178-185. https://doi.org/10.1016/j.knosys.2016.12.010
  28. Mikolov, T., I. Sutskever, K., Chen, G.S., Corrado, and J., Dean. 2013. "Distributed representations of words and phrases and their compositionality," In Advances in neural information processing systems, pp. 3111-3119.
  29. Mnih, A. and Salakhutdinov, R.R. 2008. "Probabilistic matrix factorization," In Advances in neural information processing systems, pp. 1257-1264.
  30. Mooney, R.J. and Roy, L. 2000. "Content-based book recommending using learning for text categorization," In Proceedings of the fifth ACM conference on Digital libraries, pp. 195-204.
  31. Oard, D.W.„ and Kim, J. 1998. "Implicit Feedback for Recommender Systems", In Proceedings of the 5th DELOS Workshop on Filtering and Collaborative Filtering, pp. 31-36.
  32. Parra, D. and Amatriain, X. 2011. "Walk the talk," In International Conference on User Modeling, Adaptation, and Personalization, pp. 255-268.
  33. Persson, P. 2018. "Attention manipulation and information overload," Behavioural Public Policy (2:1), pp. 78-106. https://doi.org/10.1017/bpp.2017.10
  34. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J. 1994. "GroupLens: An open architecture for collaborative filtering of netnews," In Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, pp. 175-186.
  35. Sarwar, B., Karypis, G., Konstan, J. and Riedl, J. 2001. "Item-based collaborative filtering recommendation algorithms," in Proceedings of the 10th International Conference on World Wide Web, pp. 285-295.
  36. Schafer, J.B., Frankowski, D., Herlocker, J. and Sen, S. 2007. "Collaborative filtering recommender systems," In The adaptive web, Berlin:Springer, pp. 291-324.
  37. Sikka, K., T. Wu, J., Susskind, and M., Bartlett. 2012. "Exploring bag of words architectures in the facial expression domain," In European Conference on Computer Vision, pp. 250-259.
  38. Terveen, L., Hill, W., Amento, B., McDonald, D., Creter, J. 1997. "PHOAKS: a system for sharing recommendations," Communications of the ACM (40:3), pp. 59-62. https://doi.org/10.1145/245108.245122
  39. Tewari, A.S., Kumar, A. and Barman, A.G. 2014. "Book recommendation system based on combine features of content based filtering, collaborative filtering and association rule mining," In 2014 IEEE International Advance Computing Conference, pp. 500-503.
  40. Wallach, H. M. 2006. "Topic modeling: beyond bag-of-words," In Proceedings of the 23rd international conference on Machine learning, pp. 977-984.
  41. Wu, L., S.C., Hoi, a nd N., Yu. 2010. "Semantics-preserving bag-of-words models and applications," IEEE Transactions on Image Processing (19:7), pp. 1908-1920. https://doi.org/10.1109/TIP.2010.2045169
  42. Yu, T., Mengshoel, O.J., Jude, A., Feller, E., Forgeat, J. and Radia, N. 2016. "Incremental learning for matrix factorization in recommender systems," In 2016 IEEE International Conference on Big Data, pp. 1056-1063.
  43. Zhou, Y., Wilkinson, D., Schreiber, R. and Pan, R. 2008. "Large-scale parallel collaborative filtering for the netflix prize," In International conference on algorithmic applications in management, Berlin:Springer, pp. 337-348.

Cited by

  1. 시각 정보를 활용한 딥러닝 기반 추천 시스템 vol.21, pp.3, 2020, https://doi.org/10.15813/kmr.2020.21.3.002