This paper proposes a hierarchical Bayesian model called collaborative deep learning (CDL) for recommender systems. CDL jointly performs deep representation learning for content information and collaborative filtering for the ratings (feedback) matrix, allowing two-way interaction between the two. The model is designed to address the sparsity problem in collaborative filtering by leveraging auxiliary information such as item content. CDL is a tightly coupled method that integrates deep learning with collaborative filtering, enabling the automatic learning of features from auxiliary information and naturally balancing the influence of the rating and auxiliary information. The CDL model is based on a Bayesian formulation of a stacked denoising autoencoder (SDAE), which is used to learn deep representations of content information. The model also incorporates a latent user vector and a latent item vector, which are used to capture the similarity and implicit relationships between items and users. The model is trained using an EM-style algorithm to obtain maximum a posteriori (MAP) estimates, and a sampling-based algorithm is derived for the Bayesian treatment of CDL, which turns out to be a Bayesian generalized version of back-propagation. Extensive experiments on three real-world datasets from different domains show that CDL significantly outperforms the state of the art in recommendation tasks. The model is able to handle both sparse and dense settings, and it is shown to be effective in capturing the key points of articles and user preferences. The results demonstrate that CDL can provide more accurate recommendations by leveraging deep learning and collaborative filtering in a tightly coupled manner. The model is also shown to be scalable and efficient, with the ability to handle large datasets. The paper concludes that CDL is a promising approach for recommender systems, and further research is needed to explore its potential in other applications.This paper proposes a hierarchical Bayesian model called collaborative deep learning (CDL) for recommender systems. CDL jointly performs deep representation learning for content information and collaborative filtering for the ratings (feedback) matrix, allowing two-way interaction between the two. The model is designed to address the sparsity problem in collaborative filtering by leveraging auxiliary information such as item content. CDL is a tightly coupled method that integrates deep learning with collaborative filtering, enabling the automatic learning of features from auxiliary information and naturally balancing the influence of the rating and auxiliary information. The CDL model is based on a Bayesian formulation of a stacked denoising autoencoder (SDAE), which is used to learn deep representations of content information. The model also incorporates a latent user vector and a latent item vector, which are used to capture the similarity and implicit relationships between items and users. The model is trained using an EM-style algorithm to obtain maximum a posteriori (MAP) estimates, and a sampling-based algorithm is derived for the Bayesian treatment of CDL, which turns out to be a Bayesian generalized version of back-propagation. Extensive experiments on three real-world datasets from different domains show that CDL significantly outperforms the state of the art in recommendation tasks. The model is able to handle both sparse and dense settings, and it is shown to be effective in capturing the key points of articles and user preferences. The results demonstrate that CDL can provide more accurate recommendations by leveraging deep learning and collaborative filtering in a tightly coupled manner. The model is also shown to be scalable and efficient, with the ability to handle large datasets. The paper concludes that CDL is a promising approach for recommender systems, and further research is needed to explore its potential in other applications.