Proceedings of the Annual Meeting of the Cognitive Science Society

Next point-of-interest (POI) recommendation is a challenging task that aims to recommend the next location that a user may be interested in based on their check-in trajectories. Since users travel not only with long-term stable preferences but also with short-term dynamic interests, there is often a potential dependency between long-term and short-term preferences. Most existing works tend to mine the dependencies between long-term and short-term trajectories by contrastive learning but always ignore the negative impact of the learned dependencies on the accuracy of short-term trajectory modeling. Moreover, they often only utilize the context information of the user's trajectory, while neglecting the spatiotemporal dependencies between user trajectories. To address these issues, we proposed a novel dual contrastive learning framework DCLS. Specifically, we designed a novel dual contrastive learning scheme, for which we built two views: the first view is between the user's own long-term and short-term trajectories, and the second view is between the short-term trajectories of different users. We performed contrastive learning on both views, to learn the dependency between long-term and short-term trajectories, and improve the accuracy of trajectory modeling. We also designed a multi-class attention fusion module, which integrates the spatiotemporal influence of trajectory dependencies on user mobility, enhancing the recommendation performance. We conducted extensive experiments on three real-world datasets, which demonstrated that our model achieves advanced performance in the next POI recommendation.