Graph-aware collaborative reasoning for click-through rate prediction

Click-through rate prediction(CTR) is a critical task in an online advertising system. Recently, deep learning based architectures have brought great attention in Click-through rate prediction by learning the nonlinear interaction between feature embedding of users and items. However, these methods have the following issues: (1) The collaborative information between users and items could not be fully explored due to the static embedding with lookup-table technique. (2) The learning procedure lacks cognitive reasoning about what the users want to do and what they may need. To address the above challenges, we propose a graph aware collaborative reasoning method for CTR prediction which explores the collaborative information with graph and then predicts the users' behaviors with logical reasoning. Specifically, the graph is built by the common behaviors between users, and the embedding of users and items can be learned by propagating the collaborative information in the graph. Then with the collaborative embedding of users and items, two logical operations NOT and OR are adopted to integrate the embedding for logical reasoning with the neural networks. By learning the proposed architecture in an end-to-end manner, the logical behaviors of users in the behavior sequences can be learned efficiently. Extensive experiments on five real-world datasets show that the proposed method outperforms several state-of-the-art methods in CTR prediction.