Exploring Deep Space: Learning Personalized Ranking in a Semantic Space

TL;DR

This paper introduces a novel deep learning approach that constructs a semantic space for items and learns user-specific transformations to improve personalized recommendations, outperforming existing methods on MovieLens 1M.

Contribution

It proposes a new architecture combining a semantic space with user-specific transformations, effectively utilizing content and ratings for personalized recommendations.

Findings

Significantly outperforms state-of-the-art recommenders on MovieLens 1M

Can use both content and ratings effectively

Provides a flexible framework for personalized ranking

Abstract

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.