DCT: A Deep Collaborative Filtering Approach Based on Content-Text Fused for Recommender Systems

Recommender systems commonly make recommendations by means of user-item interaction ratings. One of the basic methodologies of recommendation is collaborative filtering, which exploits users’ and items’ latent space features to make predictions of personalized ranking list for an individual user. However, most of existing collaborative filtering approaches only employ explicit interaction ratings data to predict user preferences, and neglect the necessary of exploiting implicit feedback data and auxiliary information in promoting the performance recommendation. In this paper, we raise a novel model of recommendation on the basis of neural networks architecture. Concretely, the model exploits both of interaction data and content text information as input and adopts two parallel neural networks to learn the latent feature representations of users and items for a better performance. We utilize three kinds of real-world data to make extensive evaluations on the model. The experimental results reveal that the method we proposed dramatically outperforms the state-of-the-art methodologies and achieves expressively improvement in performance.