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.

No abstract

We document a prominent abnormal stock return of –14% during the [–120, +20] day window around 482 lockup expirations in the split‐share structure reform in China. The abnormal stock returns (selling volumes) are positively (negatively) correlated with firm information transparency and postreform performance improvement, but negatively (positively) related to the level of agency problems, suggesting the existence of information‐based trading during the lockups. We present important evidence that institutional investors, especially mutual funds, possess superior information discovering capabilities than that of individual investors. Our findings confirm the information roles of lockups as a tool to signal firm quality and a commitment device to alleviate agency problems.

No abstract