Generative Interest Estimation for Document Recommendations

TL;DR

This paper introduces a content-based recommender system that models user interests as a Gaussian mixture in learned document representation space, outperforming traditional methods like LSA in predictive accuracy.

Contribution

The paper presents a novel approach combining learned document representations with a generative user interest model for improved recommendations.

Findings

Learned representations outperform LSA in predictive performance.

User interests modeled as Gaussian mixtures enable effective sampling-based recommendations.

The method demonstrates strong results on the Delicious bookmarks dataset.

Abstract

Learning distributed representations of documents has pushed the state-of-the-art in several natural language processing tasks and was successfully applied to the field of recommender systems recently. In this paper, we propose a novel content-based recommender system based on learned representations and a generative model of user interest. Our method works as follows: First, we learn representations on a corpus of text documents. Then, we capture a user's interest as a generative model in the space of the document representations. In particular, we model the distribution of interest for each user as a Gaussian mixture model (GMM). Recommendations can be obtained directly by sampling from a user's generative model. Using Latent semantic analysis (LSA) as comparison, we compute and explore document representations on the Delicious bookmarks dataset, a standard benchmark for recommender systems. We then perform density estimation in both spaces and show that learned representations outperform LSA in terms of predictive performance.