Collaborative Attentive Autoencoder for Scientific Article Recommendation

Matrix Factorization (MF) is a successful collaborative filtering approach used in recommendation systems. However, its performance decreases significantly when users of the system have limited, inadequate feedback data. This problem is also known as the data sparsity problem. To handle this problem, hybrid approaches were proposed recently to integrate items' contextual information with MF-based approaches, which improved the performance of recommendations. Nevertheless, learning better representation of the items' contents is still a challenge that needs to be further enhanced. In this paper, we propose a Collaborative Attentive Autoencoder (CATA) that learns latent factors of items through an attention mechanism that can capture the most pertinent part of information for making better recommendations. Comprehensive experiments on two real-world datasets have shown our method performs better than the state-of-the-art models according to various evaluation metrics.