Deep hybrid recommender systems via exploiting document context and statistics of items

In this paper, we propose a robust document context-aware hybrid method, which integrates convolutional neural network (CNN) into probabilistic matrix factorization (PMF) with the statistics of items to both capture contextual information and consider Gaussian noise differently. Our extensive evaluations on three real-world dataset show that our variant recommendation models based on our proposed method significantly outperform the state-of-the-art recommendation models.