Collaborative Topic Regression

• Collaborative Deep Learning for Recommender Systems
• 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.
• generalizing recent advances in deep learning from i.i.d input to non-i.i.d (CF-based) input and propose 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.