Collaborative Deep Learning for Recommender Systems

TL;DR

This paper introduces Collaborative Deep Learning (CDL), a hierarchical Bayesian model that combines deep content representation learning with collaborative filtering to improve recommender system performance, especially in sparse data scenarios.

Contribution

It proposes a novel deep learning framework that jointly models content and user feedback, outperforming existing methods like CTR in recommendation accuracy.

Findings

CDL significantly outperforms state-of-the-art methods on real-world datasets.

The model effectively handles sparse auxiliary information.

Experimental results demonstrate improved recommendation quality.

Abstract

Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recent advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix. Extensive experiments on three real-world datasets from different domains show that CDL can significantly advance the state of the art.