DOI QR코드

DOI QR Code

SKU recommender system for retail stores that carry identical brands using collaborative filtering and hybrid filtering

협업 필터링 및 하이브리드 필터링을 이용한 동종 브랜드 판매 매장간(間) 취급 SKU 추천 시스템

  • Joe, Denis Yongmin (KAIST College of Business, Korea Advanced Institute of Science and Technology (KAIST)) ;
  • Nam, Kihwan (College of Business at Korea Advanced Institute of Science and Technology (KAIST))
  • 조용민 (한국과학기술원 경영대학 경영공학부) ;
  • 남기환 (한국과학기술원 경영대학 경영공학부)
  • Received : 2017.07.21
  • Accepted : 2017.11.20
  • Published : 2017.12.31

Abstract

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.

References

  1. Al-Shamri, M.Y.H. and K.K. Bharadwaj, "Fuzzy-genetic approach to recommender systems based on a novel hybrid user model," Expert Systems with Applications, Vol.35, No.3(2008), 1386-1399. https://doi.org/10.1016/j.eswa.2007.08.016
  2. Bell, R. and Y. Koren, "Lessons from the Netflix Prize Challenge," SIGKDD Explorations, Vol.9, No.2(2007), 71-84.
  3. Billsus, D. and M. J. Pazzani, "Learning Collaborative Information Filters," Proceedings of 15th International Conference on Machine Learning, (1998), 46-45.
  4. Bobadilla, J., Ortega, F., Hernando, A., and Gutierrez, A., "Recommender systems survey," Knowledge-Based Systems, Vol.46, No.1(2013), 109-132. https://doi.org/10.1016/j.knosys.2013.03.012
  5. Burke, R. "Hybrid Recommender Systems: Survey and Experiments," User Modeling and User Adapted Interaction, Vol.12, No.4(2002), 331-370. https://doi.org/10.1023/A:1021240730564
  6. Campos, L.M., J.M. Fernandez-Luna, J.F. Huete, and M.A. Rueda-Morales, "Combining content-based and collaborative recommendations: a hybrid approach based on Bayesian Networks," International Journal of Approximate Reasoning, Vol.51, No.7(2010), 785-799. https://doi.org/10.1016/j.ijar.2010.04.001
  7. Christakou, C. and A. Stafylopatis, "A hybrid movie recommender system based on neural networks," International Conference on Intelligent Systems Design and Applications, (2005), 500-505.
  8. Gao, L.Q. and C. Li, "Hybrid personalizad recommended model based on genetic algorithm," International Conference on Wireless Communication, Networks and Mobile Computing, (2008), 9215-9218.
  9. Goldberg, D., D. Nichols, B. M. Oki, and D. Terry "Using collaborative filtering to weave an information tapestry." Communications of the ACM, Vol.35, No.12(1992), 61-70.
  10. Gong, S., "A collaborative filtering recommendation algorithm based on user clustering and item clustering," Journal of Software, Vol.5, No.7 (2010), 745-752.
  11. Herlocker, J., J. A. Konstan, R. Borschers, and J. Riedl, "An Algorithmic Framework for Performing Collaborative Filtering," Proceedings of the 22th ACM SIGIR Conf. on Research and Development in Information Retrieval,(1999), 230-237.
  12. Jeon, B. and H. Ahn, "A Collaborative Filtering System Combined with Users' Review Mining: Application to the Recommendation of Smartphone Apps," Journal of Intelligence and Information System, Vol. 21, No. 2(2015), 1-18. https://doi.org/10.13088/jiis.2015.21.2.01
  13. Kim, K.-j. and H. Ahn, "User-Item Matrix Reduction Technique for Personalized Recommender Systems," Journal of Information Technology Applications & Management, Vol. 16, No. 1(2009), 97-113.
  14. Kim, K.-j. and H. Ahn, "Collaborative Filtering with a User-Item Matrix Reduction Technique for Recommender Systems," International Journal of Electronic Commerce, Vol. 16, No. 1(2011), 107-128. https://doi.org/10.2753/JEC1086-4415160104
  15. Kim, M. and K. Kim, "Recommender Systems using Structural Hole and Collaborative Filtering," Journal of Intelligence and Information System, Vol. 20, No. 4(2014), 107-120. https://doi.org/10.13088/jiis.2014.20.4.107
  16. Lee. H. L., "A Study of the V.M.D process for sales promotion in the fashion marketing," Design Science Research, Vol.5, No.2(2002), 75-88.
  17. Lee, J. and H. S. Park, "Performance Improvement of a Movie Recommendation System using Genre-wise Collaborative Filtering," Journal of Intelligence and Information System, Vol. 13, No. 4(2007), 65-78.
  18. Lekakos, G. and G. M. Giaglis, "Improving the Prediction Accuracy of Recommendation Algorithms: Approaches Anchored on Human Factors", Interacting with Computers, Vol.1, No.18(2006), 410-431.
  19. Renaud-Deputter, S., T. Xiong, and S. Wang, "Combining collaborative filtering and clustering for implicit recommender system," Proceedings of 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA), IEEE, (2013), 748-755.
  20. Resnick, P. and H. R. Varian, "Recommender Systems," Communications of the ACM, Vol.40, No.1 (1997), 56-58.
  21. Resnick, P., N. Iacovou, M. Suchak, P. Bergstrom and J. Riedl, "Grouplens : An Open Architecture for Collaborative Filtering of Netnews," Proceedings of the ACM Conf. on Computer Supported Cooperative Work, (1994), 175-186.
  22. Sarwar, B., "Sparsity, Scalability, and Distribution in Recommender Systems," Ph.D. Diss., Dept. of Computer and Information Sciences, Univ. of Minnesota(2001).
  23. Sarwar, B., G. Karypis, J. Konstan, and J. Riedl, "Application of dimensionality reduction in recommender system - a case study" ACM WebKDD Workshop, Vol.1, No.2(2000), 264-272.
  24. Shinde, S.K. and U. Kulkami, "Hybrid personalizad recommender system using centering-bunching based clustering algorithm," Expert Systems with Applications, Vol.39, No.1(2012), 1381-1387. https://doi.org/10.1016/j.eswa.2011.08.020
  25. Ujwala H., S. R. Wanaskar, and V. D. Mukhopadhyay, "A Hybrid Web Recommendation System Based on the Improved Association Rule Mining Algorithm," Journal of Software Engineering and Applications, Vol.06, No.8(2013), 396-411. https://doi.org/10.4236/jsea.2013.68049
  26. Wen, J. and W. Zhou, "An improved item-based collaborative filtering algorithm based on clustering method," Journal of Computational Information Systems, Vol.8, No.2(2012), 571- 578.