HRAM: A Hybrid Recurrent Attention Machine for News Recommendation

Popular methods for news recommendation which are based on collaborative filtering and content-based filtering have multiple drawbacks. The former method does not account for the sequential nature of news reading and suffers from the problem of cold-start, while the latter, suffers from over-specialization. In order to address these issues for news recommendation we propose a Hybrid Recurrent Attention Machine (HRAM). HRAM consists of two components. The first component utilizes a neural network for matrix factorization. While in the second component, we first learn the distributed representation of each news article. We then use the historical data of the user in a sequential manner and feed it to an attention-based recurrent layer. Finally, we concatenate the outputs from both these components and use further hidden layers in order to make predictions. In this way, we harness the information present in the user reading history and boost it with the information available through collaborative filtering for providing better news recommendations. Extensive experiments over two real-world datasets show that the proposed model provides significant improvement over the state-of-the-art.