CFDA: Collaborative Filtering with Dual Autoencoder for Recommender System

In recent years, deep neural networks have been widely used in recommender systems. Neural collaborative filtering is a popular work to model complex interactions between users and items with deep learning. However, methods that are based on collaborative filtering usually focus on learning embedding with the factorization of pairwise interactions, thereby causing embedding to be insufficient in capturing the complex relationships between users and items. To alleviate the above problem, in this paper, we propose a novel recommendation method based on collaborative filtering with dual autoencoder (CFDA). In the proposed method, we use dual autoencoder to learn hidden representations of users and items simultaneously, and we minimize the deviation of the training data by learning the user and item representations. Extensive experiments on several datasets demonstrate that the proposed method outperforms the baseline methods that are based on neural collaborative filtering.