Boosting Recommender Systems with Deep Learning

Abstract

Farfetch is a global fashion marketplace with a catalog that, at any time, has over 200 000 products spanning over 2000 brands from luxury boutiques all around the world. Finding the right product to the right customer is a challenge that, we, as Data Scientists working on the Recommendations team, are trying to solve using state-of-the art algorithms and disruptive technologies. Deep learning (DL) is an area of Machine Learning that has recently been brought to the spotlight for its breakthrough results across several domains. In this talk, we will provide an overview of some ongoing projects in which Deep Learning methods play a major role. A common problem in online marketplaces with large catalogs such as ours is the lack of detailed metadata about the products. Particularly, features such as style, colors, pattern, occasion, sizing, etc., are known to drive customers intent but are hard to catalog manually, in a consistent way. We explain how we use our extensive dataset of normalized product images together with state of the art convolutional neural networks to extract visual features. These can then be used to provide better recommendations and improve other applications across the platform. Another application of DL is to capture relationships between products which are stylistically complementary. We leverage data from thousands of hand-curated outfits to model these intangible fashion concepts only a human specialist can provide, and generalize a method of recommending complementary products using deep siamese neural networks. This an alternative recommendation strategy that can be used to drive cross-sell opportunities. These are merely a sample of problems we are tackling using DL at Farfetch. We believe that there are plenty of opportunities for application of these techniques to recommender systems and we look forward to discussing the potentials of this stream of research with the RecSys community.

DOI: 10.1145/3109859.3109926

Cite this paper

@inproceedings{Gomes2017BoostingRS, title={Boosting Recommender Systems with Deep Learning}, author={Jo{\~a}o Gomes}, booktitle={RecSys}, year={2017} }