DDFL: A Deep Dual Function Learning-Based Model for Recommender Systems

Abstract

Over the last two decades, latent-based collaborative filtering (CF) has been extensively studied in recommender systems to match users with appropriate items. In general, CF can be categorized into two types: matching function learning-based CF and representation learning-based CF. Matching function-based CF uses a multi-layer perceptron to learn the complex matching function that maps user-item pairs to matching scores, while representation learning-based CF maps users and items into a common latent space and adopts dot product to learn their relationship. However, the dot product is prone to overfitting and does not satisfy the triangular inequality. Different from latent based CF, metric learning represents user and item into a low dimensional space, measures their explicit closeness by Euclidean distance and satisfies the triangular inequality. In this paper, inspired by the success of metric learning, we supercharge metric learning with non-linearities and propose a Metric Function Learning (MeFL) model to learn the function that maps user-item pairs to predictive scores in the metric space. Moreover, to learn the mapping more comprehensively, we further combine MeFL with a matching function learning model into a unified framework and name this new model Deep Dual Function Learning (DDFL). Extensive empirical results on four benchmark datasets are conducted and the results verify the effectiveness of MeFL and DDFL over state-of-the-art models for implicit feedback prediction.

Supported by the National Natural Science Foundation of China under Grant No. 61672252, and the Fundamental Research Funds for the Central Universities under Grant No. 2019kfyXKJC021.