  Recommender systems today have become an essential component of any
commercial website. Collaborative filtering approaches, and Matrix
Factorization (MF) techniques in particular, are widely used in recommender
systems. However, the natural data sparsity problem limits their performance
where users generally interact with very few items in the system. Consequently,
multiple hybrid models were proposed recently to optimize MF performance by
incorporating additional contextual information in its learning process.
Although these models improve the recommendation quality, there are two primary
aspects for further improvements: (1) multiple models focus only on some
portion of the available contextual information and neglect other portions; (2)
learning the feature space of the side contextual information needs to be
further enhanced. In this paper, we introduce a Collaborative Dual Attentive
Autoencoder (CATA++) for recommending scientific articles. CATA++ utilizes an
article's content and learns its latent space via two parallel autoencoders. We
employ the attention mechanism to capture the most related parts of information
in order to make more relevant recommendations. Extensive experiments on three
real-world datasets have shown that our dual-way learning strategy has
significantly improved the MF performance in comparison with other
state-of-the-art MF-based models using various experimental evaluations. The
source code of our methods is available at:
https://github.com/jianlin-cheng/CATA.
