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Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Elena Andreeva
Extraction of Visual Features for Recommendation of E-commerce Items
Data Science
(Master’s programme)
9
2017
This work is focused on the task of extracting visual features from products images and their further use in context-based recommender system.

We observed and analyzed methods of extraction of visual features currently used by major companies and online stores such as Amazon, Pinterest, Houzz.com and Flipkart.

Deep Convolutional Neural Network is used as a primary method of extraction of visual features in this work. We proposed several variations of fine-tuning the net and also modifications of the net's structure, that were implemented in practice.

We also proposed a method for forming personalized recommendations based on neural network.

The experiments were conducted on an open data set «Amazon product data», containing information about the products and the behavior of users (the facts of purchase).

The quality of the recommendations generated by the proposed system was evaluated in comparison with the «state-of-the-art» method of collaborative filtering. The best results shown by the proposed models surpassed the result of collaborative filtering on various metrics.

Keywords: Convolutional Neural Networks, Recommender Systems, Extraction of Visual Features.

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