Active Adaptation of Expert-Based Suggestions in Ladieswear Recommender System LookBooksClub via Reinforcement Learning
Fashion recommendation is one of the developing fields in e-commerce. Many different types of recommender systems exist with their own advantages and disadvantages. In this paper we create a recommender system for ladieswear that utilizes all recommender system approaches: collaborative filtering, content-based, demographic-based and knowledge-based. Using stylists’ suggestions, we created distance space for items, user clusters and connected item features to users’ characteristics. Stylist initial ratings were used to solve the cold-start problem. We adopted the Upper Conditional Bounds (UCB) algorithm for active selection of items which should be suggested. The system was designed with strong constraints dictated by the business process. The system worked for one month and estimated with 64 % of “likes” received for its suggestions, while the well-known Rocket Retail system shows only 55 % of “likes” after five years of its use.
KeywordsRecommender system Active learning Reinforcement learning Hybrid intelligent system Fashion recommendation E-commerce
Authors would like to thank Daniil Chivilikhin for useful comments. This work was financially supported by the Government of Russian Federation, Grant 074-U01.
- 1.Brusilovsky, P., Kobsa, A., Nejdl, W.: The Adaptive Web: Methods and Strategies of Web Personalization, vol. 4321. Springer Science+Business Media, Berlin (2007)Google Scholar
- 3.Burke, R.: Hybrid web recommender systems. In: The Adaptive Web, pp. 377–408. Springer, Berlin (2007)Google Scholar
- 4.Golovin, N., Rahm, E.: Reinforcement learning architecture for web recommendations. In: Proceedings of International Conference on Information Technology: Coding and Computing, 2004, ITCC 2004, vol. 1, pp. 398–402. IEEE, New York (2004)Google Scholar
- 5.Jannach, D., Friedrich, G.: Tutorial: recommender systems. In: Proceedings of the International Joint Conference on Artificial Intelligence, Barcelona (2011)Google Scholar
- 6.Kantor, P.B., Rokach, L., Ricci, F., Shapira, B.: Recommender Systems Handbook. Springer, Berlin (2011)Google Scholar
- 7.Markswebb Rank & Report: e-commerce website rank 2014. http://www.shopolog.ru/metodichka/analytics/issledovanie-e-commerce-website-rank-2014-odezhda-i-obuv (2014)
- 8.Monetate: Maximize online sales with product recommendations. http://content.monetate.com/h/i/12311883-maximize-online-sales-with-product-recommendations#axzz2IdJfsKau
- 9.Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Recommender Systems Handbook, pp. 1–35Google Scholar
- 10.Schafer, J.B., Konstan, J., Riedl, J.: Recommender systems in e-commerce. In: Proceedings of the 1st ACM Conference on Electronic Commerce, pp. 158–166. ACM, New York (1999)Google Scholar
- 11.Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Generative models for cold-start recommendations. In: Proceedings of the 2001 SIGIR Workshop on Recommender Systems, vol. 6. Citeseer (2001)Google Scholar
- 13.Sutton, R.S., Barto, A.G.: Reinforcement Learning: An IntroductionGoogle Scholar
- 14.Wang, X., Wang, Y., Hsu, D., Wang, Y.: Exploration in interactive personalized music recommendation: a reinforcement learning approach. ACM Trans. Multimedia Comput. Commun. Appl. (TOMM) 11(1), 7 (2014)Google Scholar