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.

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Mass production of ZnO nanowires, nanoribbons, and needle-like rods has been achieved by a simple method of thermal evaporation of ZnO powders mixed with graphite. Metallic catalysts, carrying gases, and vacuum conditions are not necessary. Temperature is the critical experimental parameter for the formation of different morphologies of ZnO nanostructures. Zn or Zn suboxide plays a crucial role for the nucleation of ZnO nanostructures. The as-prepared ZnO nanowires consist of single crystalline cores and thin amorphous shells. As determined by electron diffraction, the growth direction of ZnO nanowires is [001], which has no orientation relationship with the substrate. A strong room-temperature photoluminescence in ZnO nanostructures has been demonstrated.