关键词: 协同过滤, 深度学习, 辅助信息, 多层交互, 神经网络, 推荐系统

Abstract: In the traditional recommendation systems, the approach of matrix factorization collaborative filtering only just considers the single information of rating, as a shallow model, it can hardly learn deeper feature information. This paper proposes a multi-interaction deep matrix factorization model based on auxiliary information, firstly through deep learning model and merge more auxiliary information as input, effectively alleviates the problem of data sparsity. Then, the structure of multi-interactive non-linear network is leveraged to learn the deep feature representation of more abstract and dense; through inner product interactions on the latent features of users and items repeatedly, it obtains the different layers of feature representation results; finally it aggregates all the interaction results to predict. The experiment results on the Movielens latest 100K dataset show that the proposed model is over the state-of-the-art methods in RMSE.

Key words: collaborative filtering, deep learning, auxiliary information, multi-interaction, neural network, recommendation system