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A sequence-based personalized service for the short life cycle products

수명주기가 짧은 상품들에 대한 시퀀스 기반 개인화 서비스

  • 최주철 (경희대학교 창업보육센타)
  • Received : 2017.11.02
  • Accepted : 2017.12.20
  • Published : 2017.12.28

Abstract

Most new products not only suddenly disappear in the market but also quickly cannibalize older products. Under such a circumstance, retailers may have too much stock, and customers may be faced with difficulties discovering products suitable to their preferences among short life cycle products. To address these problems, recommender systems are good solutions. However, most previous recommender systems had difficulty in reflecting changes in customer preferences because the systems employ static customer preferences. In this paper, we propose a recommendation methodology that considers dynamic customer preferences. The proposed methodology consists of dynamic customer profile creation, neighborhood formation, and recommendation list generation. For the experiments, we employ a mobile image transaction dataset that has a short product life cycle. Our experimental results demonstrate that the proposed methodology has a higher quality of recommendation than a typical collaborative filtering-based system. From these results, we conclude that the proposed methodology is effective under conditions where most new products have short life cycles. The proposed methodology need to be verified in the physical environment at a future time.

Keywords

Short life cycle product;Personalized service;Sequence;Preferences;Recommender system

References

  1. Nielsen, Every breakthrough product needs an audience,. http://www.nielsen.com/content/dam/corporate/u/en/NielsenGlobalNewProductsReportFINAL.pdf, 2013.
  2. T. Higuchi and M. D. Troutt., "Dynamic simulation of the supply chain for a short life cycle product-Lessons from the Tamagotchi case." Computers & Operations Research, Vol. 31, No. 7, pp. 1097-1114, 2004. https://doi.org/10.1016/S0305-0548(03)00067-4
  3. Amazon - http://www.amazon.com
  4. Netflix- http://www.netflix.com
  5. S. H. Park, and Y. H. Kim, "User recognition based TV programs recommendation system in smart devices environment." Journal of Digital Convergence, Vol. 11, No. 1, pp. 249-254, 2013.
  6. J. T. Oh, and S. Y. Lee, "Fuzzy-AHP based mobile games recommendation system using Bayesian network." Journal of Digital Convergence, Vol. 15, No. 4, pp. 461-464, 2017.
  7. L. Lu, M. Medo, C. H. Yeung, Y. C. Zhang, Z. K., Zhang, and T. Zhou, "Recommender systems." Physics Reports, Vol. 519, No. 1, pp. 1-49, 2012. https://doi.org/10.1016/j.physrep.2012.02.006
  8. C, Tseng, "Portfolio management using hybrid recommendation system." In e-Technology, e-Commerce and e-Service, 2004. EEE'04. 2004 IEEE International Conference on IEEE, pp.202-206, 2004,
  9. G. J, Kim, and J. S. Han, "Application method of task ontology technology for recommendation of automobile parts." Journal of Digital Convergence, Vol. 10, No. 6, pp. 275-281, 2012.
  10. H. J. Ahn, "A new similarity measure for collaborative filtering to alleviate the new customer cold-starting problem." Information Sciences, Vol. 178. No. 1, pp. 37-51, 2008, https://doi.org/10.1016/j.ins.2007.07.024
  11. Z. Huang, D. Zeng, and H. Chen, "A comparison of collaborative-filtering recommendation algorithms for e-commerce." IEEE Intelligent Systems, Vol. 22, No. 5, pp. 68-78, 2007. https://doi.org/10.1109/MIS.2007.4338497
  12. D. Jia., F. Zhang, and S. Liu., "A robust collaborative filtering recommendation algorithm based on multidimensional trust model." Journal of Software, Vol. 8, No. 1, pp. 11-18. 2013.
  13. C. F. Tsai, and C. Hung, "Cluster ensembles in collaborative filtering recommendation." Applied Soft Computing, Vol. 12, No.4, pp. 1417-1425. 2012. https://doi.org/10.1016/j.asoc.2011.11.016
  14. K, Yu, A. Schwaighofer, V. Tresp, X. Xu, and H. P. Kriegel, "Probabilistic memory-based collaborative filtering." Knowledge and Data Engineering, IEEE Transactions on, Vol. 16, No. 1, pp.56-69, 2004. https://doi.org/10.1109/TKDE.2004.1264822
  15. Zou. T, Wang. Y, Wei. X, Li. Z, and Yang. G, "An effective collaborative filtering via enhanced similarity and probability interval prediction." Intelligent Automation & Soft Computing, Vol. 20, No. 4, pp. 555-566, 2014, https://doi.org/10.1080/10798587.2014.934598
  16. G. Adomavicius., R. Sankaranarayanan, S. Sen, and A. Tuzhilin, "Incorporating contextual information in recommender systems using a multidimensional approach." ACM Transactions on Information Systems (TOIS), Vol. 23, No. 1, pp. 103-145, 2005. https://doi.org/10.1145/1055709.1055714
  17. H. Drachsler, H. G. Hummel, and R. Koper, "Personal recommender systems for learners in lifelong learning networks: the requirements, techniques and model." International Journal of Learning Technology, Vol. 3, No. 4, pp.404-423, 2008. https://doi.org/10.1504/IJLT.2008.019376
  18. P. Han, B. Xie, F. Yang., and R. Shen, "A scalable P2P recommender system based on distributed collaborative filtering." Expert systems with applications, Vol. 27, No. 2, pp. 203-210, 2004. https://doi.org/10.1016/j.eswa.2004.01.003
  19. H. K. Kim, J. K. Kim, and Y. U. Ryu, "Personalized recommendation over a customer network for ubiquitous shopping." Services Computing, IEEE Transactions on, Vol. 2, No. 2, pp. 140-151, 2009. https://doi.org/10.1109/TSC.2009.7
  20. B. Lika, K. Kolomvatsos., and S. Hadjiefthymiades, "Facing the cold start problem in recommender systems." Expert Systems with Applications, Vol. 41, No. 4, pp. 2065-2073, 2014. https://doi.org/10.1016/j.eswa.2013.09.005
  21. B. Sarwar, G. Karypis., J. Konstan., and J. Riedl, "Analysis of recommendation algorithms for e-commerce." In Proceedings of the 2nd ACM conference on Electronic commerce, ACM, pp. 158-167, 2000.
  22. C. X. Yin, and Q. K. Peng, "A careful assessment of recommendation algorithms related to dimension reduction techniques." Knowledge-Based Systems, Vol. 27, pp. 407-423, 2012. https://doi.org/10.1016/j.knosys.2011.11.022
  23. J. Eicher, S. Evenson, and H. Lutz, , The visible self. Fairchild Publications, New York, 2000.
  24. S. U. Rahman, S. Saleem., S. Akhtar, T. Ali, and M. A. Khan, "Consumers' Adoption of Apparel Fashion: The Role of Innovativeness, Involvement, and Social Values." International Journal of Marketing Studies, Vol. 6, No. 3, p49. 2014.
  25. S. J. Oh, and J-Y. Kim, "A hierarchical clustering algorithm for categorical sequence data." Information processing letters, Vol. 91, No. 3, pp. 135-140, 2004. https://doi.org/10.1016/j.ipl.2004.04.002
  26. Y. B. Cho, Y. H. Cho, and S. H. Kim, "Mining changes in customer buying behavior for collaborative recommendations." Expert Systems with Applications, Vol. 28, No. pp. 359-369, 2005. https://doi.org/10.1016/j.eswa.2004.10.015
  27. H. Wang, and S. Wang, "Mining purchasing sequence data for online customer segmentation." International Journal of services operations and informatics, Vol. 2, No. 4, pp 382-390, 2007. https://doi.org/10.1504/IJSOI.2007.015641
  28. Y. Zhang, and J. Cao, "Personalized recommendation based on behavior sequence similarity measures." In Behavior and Social Computing, Springer International Publishing. pp. 165-177, 2013.
  29. P. Childerhouse, and D. Towill, "Engineering supply chains to match customer requirements." Logistics information management, vol .13. No. 6, pp.337-346, 2000. https://doi.org/10.1108/09576050010355635
  30. A. A. Kurawarwala, and H. Matsuo, "Product growth models for medium-term forecasting of short life cycle products." Technological Forecasting and Social Change, Vol. .57, No. 3, pp. 169-196, 1998. https://doi.org/10.1016/S0040-1625(97)00102-9
  31. X. H. Xu, and Q. Z. Song, "Forecasting for products with short life cycle based on improved BASS model." Industrial Engineering and Management, Vol. 5, pp. 27-31, 2007.
  32. C. V. Trappey, and H. Y. Wu, "An evaluation of the time-varying extended logistic, simple logistic, and Gompertz models for forecasting short product lifecycles." Advanced Engineering Informatics, Vol. 22, No. 4, pp. 421-430, 2008. https://doi.org/10.1016/j.aei.2008.05.007
  33. T. Higuchi, and M. D. Troutt, Life cycle management in supply chains: Identifying innovations through the case of the VCR. Hershey, PA: IGI Publishing. 2008.
  34. B. Liu, J. Chen, S. Liu, and R. Zhang, "Supply-chain coordination with combined contract for a short-life-cycle product." Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, Vol. 36, No. 1, pp. 53-61, 2006. https://doi.org/10.1109/TSMCA.2005.859172
  35. G. E. Yap, X. L. Li, and S. Y. Philip, "Effective next-items recommendation via personalized sequential pattern mining." In Database Systems for Advanced Applications, Springer Berlin Heidelberg, pp. 48-64, 2012.
  36. N. Hariri, B. Mobasher, and R. Burke, "Context-aware music recommendation based on latenttopic sequential patterns." In Proceedings of the sixth ACM conference on Recommender systems, ACM, pp. 131-138. 2012.
  37. H. S. Moon, J. K. Kim, and Y. U. Ryu, "A sequence-based filtering method for exhibition booth visit recommendations." International Journal of Information Management, Vol. 33, No. 4, pp. 620-626, 2013. https://doi.org/10.1016/j.ijinfomgt.2013.03.004
  38. Salehi. M, Kamalabadi I. N, and Ghoushchi. M. B. G, "Personalized recommendation of learning material using sequential pattern mining and attribute based collaborative filtering." Education and Information Technologies, Vol. 19, No. 4, pp.713-735, 2014. https://doi.org/10.1007/s10639-012-9245-5