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Regular version of the site

Book chapter

Extraction of Visual Features for Recommendation of Products via Deep Learning

P. 201-210.
Andreeva E., Ignatov D. I., Grachev A., Savchenko A.

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.