APL: Adversarial Pairwise Learning for Recommender Systems

The main objective of recommender systems is to help users select their desired items, where a major challenge is modeling users' preferences based on their historical feedback (e.g., clicks, purchases or check-ins). Recently, several recommendation models have utilized the adversarial technique, which has been successfully used to capture real data distributions in various domains (e.g., computer vision). Nevertheless, the training process of the original adversarial technique is very slow and unstable in the domain of recommender systems. First, the sparsity of the implicit feedback dataset aggravates the inherently intractable adversarial training process. Second, since the original adversarial model is designed for differentiable values (e.g., images), the discrete items also increase the training difficulty. To cope with these issues, we propose a novel method named Adversarial Pairwise Learning (APL), which unifies generative and discriminative models via adversarial learning. Specifically, based on the weaker assumption that the user prefers observed items over generated items, APL exploits pairwise ranking to accelerate the convergence and enhance the stability of adversarial learning. Additionally, a differentiable procedure is adopted to replace the discrete item sampling to optimize APL via backpropagation and stabilize the training process. Extensive experiments under multiple recommendation scenarios demonstrate APL's effectiveness, fast convergence and stability. Our implementation of APL is available at: https://github.com/ZhongchuanSun/APL. (C) 2018 Elsevier Ltd. All rights reserved.