Variational autoencoders with Gaussian mixture prior for recommender systems

Abstract

Recommender systems are used everywhere, from search engines to entertainment websites like video or audio streaming platforms. They are essential when the amount of information available is substantial by providing users with the right information at the right time. The standard approach involves gathering the impressions of users based on content to create the next recommendation. However, collecting these impressions is costly and is not always accurate. A different alternative is to simply gather the binary interactions between a user and the content. Radio-Canada, the official French broadcaster in Canada, has collected these types of interactions for its video streaming platform called “Tou.TV”. They asked us to create a novel recommender system using temporal and contextual signals. In this thesis, we present two hybrid systems based on a variational autoencoder (VAE) architecture with a Gaussian mixture prior to better understand the latent space with multiple Gaussian distributions. We apply these systems on the Tou.TV dataset, but also demonstrate the efficacy of our approach on the popular dataset called MovieLens-20M. These systems with the simple enhancements proposed on the traditional VAE architecture are able to outperform popular off-the-shelf models.

Date	29 Jun 2020
Original language	American English
Awarding Institution	École de technologie supérieure
Supervisor	Patrick Cardinal (Supervisor) & Marco Pedersoli (Co-supervisor)