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Extraction of Visual Features for Recommendation of Products via Deep Learning

  • Elena AndreevaEmail author
  • Dmitry I. Ignatov
  • Artem Grachev
  • Andrey V. Savchenko
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11179)

Abstract

In this paper (The first author is the 1st place winner of the Open HSE Student Research Paper Competition (NIRS) in 2017, Computer Science nomination, with the topic “Extraction of Visual Features for Recommendation of Products”, as alumni of 2017 “Data Science” master program at Computer Science Faculty, HSE, Moscow), we describe a special recommender approach based on features extracted from the clothes’ images. The method of feature extraction relies on pre-trained deep neural network that follows transfer learning on the dataset. Recommendations are generated by the neural network as well. All the experiments are based on the items of category Clothing, Shoes and Jewelry from Amazon product dataset. It is demonstrated that the proposed approach outperforms the baseline collaborative filtering method.

Keywords

Visual feature Recommender system Fine tuning Collaborative filtering 

Notes

Acknowledgements

This study is supported by Russian Federation President grant MD-306.2017.9. Andrey V. Savchenko is supported by the Laboratory of Algorithms and Technologies for Network Analysis, National Research University Higher School of Economics. The work of Dmitry I. Ignatov (contributed to Sects. 1, 2, 3 and 6) was supported by the Russian Science Foundation under grant 17-11-01294 and performed at National Research University Higher School of Economics, Russia. The authors would like to thank Dmitry Soloviev from Mail.ru and Gleb Gusev from Yandex team for their piece of advice and relevant literature on the problem.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Elena Andreeva
    • 1
    • 3
    Email author
  • Dmitry I. Ignatov
    • 1
  • Artem Grachev
    • 1
  • Andrey V. Savchenko
    • 2
  1. 1.National Research University Higher School of EconomicsMoscowRussia
  2. 2.National Research University Higher School of EconomicsNizhniy NovgorodRussia
  3. 3.ex Mail.Ru GroupMoscowRussia

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