Recommender systems leverage both content and user interactions to generate recommendations that fit users' preferences. The recent surge of interest in deep learning presents new opportunities for exploiting these two sources of information. To recommend items we propose to first learn a user-independent high-dimensional semantic space in which items are positioned according to their substitutability, and then learn a user-specific transformation function to transform this space into a ranking according to the user's past preferences. An advantage of the proposed architecture is that it can be used to effectively recommend items using either content that describes the items or user-item ratings. We show that this approach significantly outperforms state-of-the-art recommender systems on the MovieLens 1M dataset.
Other works like REF are focused on learning users preferences in a high-dimensional semantic latent space, with the advantage of being able to recommend items using content that describes the items.
Exploring Deep Space: Learning Personalized Ranking in a Semantic Space
DLRS 2016
Computer Science