POI Recommendation via Multi-Objective Adversarial Imitation Learning

Point-of-Interest (POI) recommendation aims to predict users' future locations based on their historical check-ins. Despite the success of recent deep learning approaches in capturing POI semantics and user behavior, they continue to face the persistent problem of data sparsity and incompleteness. In this paper, we introduce Multi-Objective Adversarial Imitation Recommender (MOAIR), a novel framework that integrates Generative Adversarial Imitation Learning with multi-objective to address this issue. MOAIR effectively captures user behavior patterns and spatial-temporal contextual information via graph-enhanced self-supervised state encoder and overcomes data sparsity by robustly learning from limited data and generating diverse samples. By accommodating diverse user patterns in the training data, the framework also mitigates the typical mode-collapse issue in generative adversarial learning and thus enhances the overall performance. MOAIR employs a multi-objective imitation learning architecture where the imitation learning agent (IL agent) explores the POI space and receives multifaceted reward signals. Utilizing the Paralleled Proximal Policy Optimization (3PO) framework to optimize multi-objectives, the IL agent ensures efficient and stable policy updates. Additionally, to address the issue of high noise in POI recommendation scenarios, we use a novel generative way to define our policy net and incorporate a variational bottleneck for regularization to enhance the stability of adversarial learning. Comprehensive experiments reveal the superior performance for MOAIR compared to other baseline approaches, especially with sparse training data.