CATA++: A Collaborative Dual Attentive Autoencoder Method for Recommending Scientific Articles

1 code implementation • 27 Feb 2020 • Meshal Alfarhood, Jianlin Cheng

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.

Collaborative Filtering Recommendation Systems