Active Adaptation of Expert-Based Suggestions in Ladieswear Recommender System LookBooksClub via Reinforcement Learning

  • Nikita Golubtsov
  • Daniel Galper
  • Andrey Filchenkov
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 449)

Abstract

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.

Keywords

Recommender system Active learning Reinforcement learning Hybrid intelligent system Fashion recommendation E-commerce 

References

  1. 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
  2. 2.
    Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Inter. 12(4), 331–370 (2002)CrossRefMATHGoogle Scholar
  3. 3.
    Burke, R.: Hybrid web recommender systems. In: The Adaptive Web, pp. 377–408. Springer, Berlin (2007)Google Scholar
  4. 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. 5.
    Jannach, D., Friedrich, G.: Tutorial: recommender systems. In: Proceedings of the International Joint Conference on Artificial Intelligence, Barcelona (2011)Google Scholar
  6. 6.
    Kantor, P.B., Rokach, L., Ricci, F., Shapira, B.: Recommender Systems Handbook. Springer, Berlin (2011)Google Scholar
  7. 7.
  8. 8.
  9. 9.
    Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Recommender Systems Handbook, pp. 1–35Google Scholar
  10. 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. 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
  12. 12.
    Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 4 (2009)CrossRefGoogle Scholar
  13. 13.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning: An IntroductionGoogle Scholar
  14. 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

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Nikita Golubtsov
    • 1
  • Daniel Galper
    • 1
  • Andrey Filchenkov
    • 1
  1. 1.ITMO UniversitySt. PetersburgRussia

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