Diff-DGMN: A Diffusion-Based Dual Graph Multiattention Network for POI Recommendation

Effective Point-of-Interest (POI) recommendation systems play a pivotal role in modern location-aware applications and human mobility, facilitating customized suggestions for users’ upcoming exploration destinations. Understanding the intricate dynamics of user movement, which are often influenced by a multitude of factors, remains a formidable task. Moreover, discrepancies between the acquired representation distribution and the authentic target distribution of user interests also present a notable obstacle. To tackle these problems, we make an attempt to bridge the gap by introducing diffusion models and propose a diffusion-based dual graph multiattention network (Diff-DGMN). Specifically, we have constructed two types of graphs: one is a user-oriented local POI transition graph and the other is a global-based POI distance graph. Subsequently, we put forward two graph learning representation modules to capture the sequential encoding of users and the geographic representations of nodes, respectively. Furthermore, an attention-based location prototype generation module is introduced to merge the captured sequential encoding and geographic representation, yielding richer semantic interaction features. In the end, we obtain the final results by leveraging the forward diffusion process and corresponding its reverse-time generation to sample users’ future preferences from the posterior distribution. Our Diff-DGMN model demonstrates its remarkable recommendation performance through extensive experimentation on five real-world data sets. Compared with the most state-of-the-art methodologies, Diff-DGMN has improved performance in accuracy, normalized discounted cumulative gain (NDCG), and mean reciprocal rank (MRR) by 8.04%, 8.63%, and 9.09%, respectively. Our codes are available at https://github.com/JKZuo/Diff-DGMN.