Implementing Content-Based Recommender System for Fashion Industry Using Deep Neural Networks

Student: Girkin Valeriy

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2018

Recommendation systems can cope with the growing volume and complexity of data. In recent years, the revolutionary achievements of deep learning in various fields have attracted a lot of attention and the field of recommender systems has not been left out. In this paper, we plan to study the application of methods of deep learning in relation to the fashion industry, building a content-based "item-item" recommender system. We show why these methods can be applied in this context, as well as the urgency of the recommender problem for the fashion industry. Next, we describe the tools and approaches we have studied, and the data available to us. In the end, we will describe the recommender system we have built and its possible improvements.

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