Collaborative Deep Learning for Recommender Systems

“To address the challenges above, we develop a hierarchical Bayesian model called collaborative deep learning (CDL) as a novel tightly coupled method for RS. We first present a Bayesian formulation of a deep learning model called stacked denoising autoencoder (SDAE) [32]. With this, we then present our CDL model which tightly couples deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix, allowing two-way interaction between the two. Experiments show that CDL significantly outperforms the state of the art. Note that although we present CDL as using SDAE for its feature learning component, CDL is actually a more general framework which can also admit other deep learning models such as deep Boltzmann machines [25], recurrent neural networks [10], and convolutional neural networks [16]. The main contribution of this paper is summarized below:  

By performing deep learning collaboratively, CDL can simultaneously extract an effective deep feature representation from content and capture the similarity and implicit relationship between items (and users). The learned representation may also be used for tasks other than recommendation.

Unlike previous deep learning models which use simple target like classification [15] and reconstruction [32], we propose to use CF as a more complex target in a probabilistic framework.  

Besides the algorithm for attaining maximum a posteriori (MAP) estimates, we also derive a sampling-based algorithm for the Bayesian treatment of CDL, which, interestingly, turns out to be a Bayesian generalized version of back-propagation.

To the best of our knowledge, CDL is the first hierarchical Bayesian model to bridge the gap between state- of-the-art deep learning models and RS.

Besides, due to its Bayesian nature, CDL can be easily extended to incorporate other auxiliary information to further boost the performance.   Extensive experiments on three real-world datasets from different domains show that CDL can significantly advance the state of the art.”