KIISE Transactions on Computing Practices (정보과학회 컴퓨팅의 실제 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Rapid Hybrid Recommender System with Web Log for Outbound Leisure Products

웹로그를 활용한 고속 하이브리드 해외여행 상품 추천시스템

  • 이규식 (고려대학교 정보보호대학원 빅데이터응용및보안) ;
  • 윤지원 (고려대학교 정보보호대학원)
  • Received : 2016.08.19
  • Accepted : 2016.09.20
  • Published : 2016.12.15
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Abstract

Outbound market is a rapidly growing global industry, and has evolved into a 11 trillion won trade. A lot of recommender systems, which are based on collaborative and content filtering, target the existing purchase log or rely on studies based on similarity of products. These researches are not highly efficient as data was not obtained in advance, and acquiring the overwhelming amount of data has been relatively slow. The characteristics of an outbound product are that it should be purchased at least twice in a year, and its pricing should be in the higher category. Since the repetitive purchase of a product is rare for the outbound market, the old recommender system which profiles the existing customers is lacking, and has some limitations. Therefore, due to the scarcity of data, we suggest an improved customer-profiling method using web usage mining, algorithm of association rule, and rule-based algorithm, for faster recommender system of outbound product.

해외여행시장은 매년 가파르게 성장하고 있는 산업중 하나이며 2016년 11조의 시장을 형성하고 있다. 거대한 시장형성과는 달리 해외여행상품 추천에 대한 국내연구는 전무한 상태이다. 많은 상품 추천 방법들이(협업적 필터링, 내용기반 필터링) 기존 구매 내역을 대상으로 하거나 혹은 상품의 유사성을 이용한 연구들이 주를 이루고 있다. 이러한 연구들은 연산할 데이터의 양이 많아질 경우 속도의 저하와 데이터가 충분히 확보되지 못한 상황 하에서는 좋은 성능을 보여주지 못하고 있다. 해외 여행상품의 특성상 1-2년에 한번정도의 구매패턴과 상품들의 가격대가 상대적으로 높으며, 동일 상품의 구매가 거의 없는 특징이 있기 때문에 일반적인 상품추천 시스템의 고객 프로파일링 방법으로는 적용에 한계가 있다. 이에 웹사용성(Web Usage Mining)을 통한 고객 프로파일링 기법, 데이터의 희소성 문제를 해결하기 위한 연관규칙 알고리즘과 규칙 기반 알고리즘을 결합하여 고속의 상품 추천시스템 방법을 제안한다. 본 논문에서는 연관규칙 방법에서 가장 많이 사용되어지는 Apriori 방법, 규칙기반 방법(Rule Base) 과 실제 여행사의 웹로그를 사용하여 46%라는 높은 추천 성능의 결과를 검증하였으며, 상품의 개수와 고객의 수가 상품추천 처리 속도에 영향을 주지 않으며, 실제 커머셜한 환경 하에서도 1초이내에 상품을 추천해줄 수 있는 결과를 보여준다.

Keywords

References

  1. Yong Kim, "A Study on Design and Implementation of Personalized Information Recommendation System based on Apriori Algorithm," KBSLIS, Vol. 23, pp. 283-308, 2012.
  2. Ji Soo Park, Suk Jae Jeong, Young Hoon Lee, Kyung Sup Kim, "Implementation of Rule Based Insurance Product Recommend and Design System using Fuzzy Inference," The Journal of Society for e-Business Studies, Vol. 12, No. 1, pp. 99-122, 2007.
  3. Hyung Bong Lee, "A Comparative Analysis on Mining Association Rules Algorithms," KSIAM, IT series, Vol. 9, No. 1, pp. 69-82, 2005.
  4. Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, John Riedl, "GroupLens: An Open Architecture for Collaborative Filtering of Netnews," Proc. of ACM 1994 Conference on Computer Supported Cooperative Work, Chapel Hill, NC: pp. 175-186.
  5. O-Joun Lee, "Hybrid Preference Prediction Technique Using Weighting based Data Reliability for Collaborative Filtering Recommendation System," Journal of the Korea Society of Computer and Information, 2014-05 19:61-69.
  6. Srivastava, Jaideep, et al., "Web usage mining: Discovery and applications of usage patterns from web data," Acm Sigkdd Explorations Newsletter 1.2 (2000): 12-23. https://doi.org/10.1145/846183.846188
  7. Ji-Hye Kim, Doo-Soon Park, "Development of the Goods Recommendation System using Association Rules and Collaborating Filtering," The Journal of Korean association of computer education, Vol. 9, pp. 71-80, 2006.
  8. Unil Yun, Gwangbum Pyun, "Performance analysis of Frequent Itemset Mining Technique based on Transaction Weight Constraints," Journal of Korean Society for Internet Information, Vol. 16, No. 1, pp. 67-74, Feb. 2015.
  9. Shu-hsien Liao, Yin-Ju Chen, Min-yi Deng, "Mining customer knowledge for tourism new product development and customer relationship management," Expert Systems With Applications, Vol. 37, No. 6, pp. 4212-4223, 2010. https://doi.org/10.1016/j.eswa.2009.11.081
  10. Joel P. Lucas, Nuno Luz, Maria N. Moreno, Ricardo Anacleto, Ana Almeida Figueiredo, Constantino Martins, "A hybrid recommendation approach for a tourism system," Expert Systems With Applications, Jul. Vol. 40, No. 9, pp. 3532-3550, 2013. https://doi.org/10.1016/j.eswa.2012.12.061
  11. Bamshad Mobasher, Honghua Dai, Tao Luo, Miki Nakagawa, "Effective Personalization Based on Association Rule Discovery from Web Usage Data," International workshop on web information and data management; (WIDM) / 2001, pp. 9-15, 2001.
  12. il Woo, "Shopping Recommendation System using Collaborative Filtering," Inha Univercity, May, 2014.
  13. Minjeong Kim․Yoonho Cho, "A Multimodal Profile Ensemble Approach to Development of Recommender Systems Using Big Data," Journal of Intelligent Information Systems, Vol. 21, No. 4, Dec. pp. 93-110, 2015. (18 pages)
  14. Kim, Byeong Man, et al., "A new approach for combining content-based and collaborative filters," Journal of Intelligent Information Systems, Vol. 27, No. 1, pp. 79-91, 2006. https://doi.org/10.1007/s10844-006-8771-2
  15. Kyung-Yong Chung, Sun-Moon Jo, "Discovery of Preference through Learning Profile for Contentbased Filtering," Journal of the Korea Contents Association, Vol. 8, No. 2, 1-8, Dec. 2008. (8 pages) https://doi.org/10.5392/JKCA.2008.8.2.001
  16. In-Yong Jeong, Xitong Yang, Hoe-Kyung Jung, "A Study on Movies Recommendation System of Hybrid Filtering-Based," The Korea Institute of Information and Communication Engineering, Vol. 19, No. 1, pp. 113-118, Jan. 2015. (6 pages) https://doi.org/10.6109/jkiice.2015.19.1.113
  17. wikipedia, "Apriori algorithm," [Online]. Available: http://en.wikipedia.org/wiki/Apriori_algorithm

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