Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. ... more 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. Inspired by recent success demonstrated by deep learning models, we propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which tightly couples a Bayesian formulation of the stacked denoising autoencoder and probabilistic matrix factorization. Extensive experiments on real-world datasets show that CDL can significantly advance the state of the art.
Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. ... more 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. Inspired by recent success demonstrated by deep learning models, we propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which tightly couples a Bayesian formulation of the stacked denoising autoencoder and probabilistic matrix factorization. Extensive experiments on real-world datasets show that CDL can significantly advance the state of the art.
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Papers by Kai Gao