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)


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.


Recommender 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.


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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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