Variational Collaborative Learning for User Probabilistic Representation

TL;DR

This paper introduces a novel variational autoencoder-based collaborative learning model that enables synchronous, Bayesian probabilistic user representation learning, effectively addressing cold start and data sparsity in recommender systems.

Contribution

The paper proposes VCM, a new model with two linked VAEs for synchronous collaborative learning, improving upon asynchronous methods in hybrid recommendation systems.

Findings

VCM outperforms state-of-the-art methods on three real datasets.

The model effectively addresses cold start and data sparsity issues.

Synchronous learning enhances the quality of user representations.

Abstract

Collaborative filtering (CF) has been successfully employed by many modern recommender systems. Conventional CF-based methods use the user-item interaction data as the sole information source to recommend items to users. However, CF-based methods are known for suffering from cold start problems and data sparsity problems. Hybrid models that utilize auxiliary information on top of interaction data have increasingly gained attention. A few "collaborative learning"-based models, which tightly bridges two heterogeneous learners through mutual regularization, are recently proposed for the hybrid recommendation. However, the "collaboration" in the existing methods are actually asynchronous due to the alternative optimization of the two learners. Leveraging the recent advances in variational autoencoder~(VAE), we here propose a model consisting of two streams of mutual linked VAEs, named variational collaborative model (VCM). Unlike the mutual regularization used in previous works where two learners are optimized asynchronously, VCM enables a synchronous collaborative learning mechanism. Besides, the two stream VAEs setup allows VCM to fully leverages the Bayesian probabilistic representations in collaborative learning. Extensive experiments on three real-life datasets have shown that VCM outperforms several state-of-art methods.