Conditional Restricted Boltzmann Machines for Cold Start Recommendations
This addresses the cold start issue for new items in recommender systems, offering an incremental improvement by integrating content and collaborative data.
The paper tackles the cold start problem in recommender systems by applying Conditional Restricted Boltzmann Machines (CRBMs) to incorporate extra information, showing that CRBMs perform well for rating prediction and can be favorably compared with matrix factorization models.
Restricted Boltzman Machines (RBMs) have been successfully used in recommender systems. However, as with most of other collaborative filtering techniques, it cannot solve cold start problems for there is no rating for a new item. In this paper, we first apply conditional RBM (CRBM) which could take extra information into account and show that CRBM could solve cold start problem very well, especially for rating prediction task. CRBM naturally combine the content and collaborative data under a single framework which could be fitted effectively. Experiments show that CRBM can be compared favourably with matrix factorization models, while hidden features learned from the former models are more easy to be interpreted.