Graph neural network (GNN) based recommender systems have become one of the mainstream trends due to the powerful learning ability from user behavior data. Understanding the user intents from behavior data is the key to recommender systems, which poses two basic requirements for GNN-based recommender systems. One is how to learn complex and diverse intents especially when the user behavior is usually inadequate in reality. The other is different behaviors have different intent distributions, so how to establish their relations for a more explainable recommender system. In this paper, we present the Intent-aware Recommendation via Disentangled Graph Contrastive Learning (IDCL), which simultaneously learns interpretable intents and behavior distributions over those intents. Specifically, we first model the user behavior data as a user-item-concept graph, and design a GNN based behavior disentangling module to learn the different intents. Then we propose the intent-wise contrastive learning to enhance the intent disentangling and meanwhile infer the behavior distributions. Finally, the coding rate reduction regularization is introduced to make the behaviors of different intents orthogonal. Extensive experiments demonstrate the effectiveness of IDCL in terms of substantial improvement and the interpretability.

翻译：基于图神经网络（GNN）的推荐系统因从用户行为数据中强大的学习能力而成为主流趋势之一。从行为数据中理解用户意图是推荐系统的关键，这对基于GNN的推荐系统提出了两个基本要求：一是如何学习复杂且多样的意图，尤其是在用户行为通常不充分的现实场景中；二是不同行为具有不同的意图分布，因此如何建立它们之间的关系以构建更可解释的推荐系统。本文提出基于解耦图对比学习的意图感知推荐系统（IDCL），该方法同时学习可解释的意图及其上的行为分布。具体而言，我们首先将用户行为数据建模为用户-物品-概念图，并设计基于GNN的行为解耦模块以学习不同意图；随后提出意图级对比学习增强意图解耦并推断行为分布；最后引入编码率降低正则化使不同意图的行为正交化。大量实验证明了IDCL在性能显著提升和可解释性方面的有效性。