{"pk":24380,"title":"Dual Contrastive Learning for Next POI Recommendation with Long and Short-Term Trajectory Modeling","subtitle":null,"abstract":"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.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"Artificial Intelligence; Sociology; Behavioral Science; Decision making; Machine learning; Predictive Processing; Comparative Analysis; Computational Modeling; Computer-based experiment"}],"section":"Papers with Poster Presentation","is_remote":true,"remote_url":"https://escholarship.org/uc/item/9g25f3sx","frozenauthors":[{"first_name":"Zhi","middle_name":"","last_name":"Liu","name_suffix":"","institution":"Zhejiang University of Technology","department":""},{"first_name":"Junhui","middle_name":"","last_name":"Deng","name_suffix":"","institution":"College of Computer Science and Technology","department":""},{"first_name":"Deju","middle_name":"","last_name":"Zhang","name_suffix":"","institution":"College of Computer Science and Technology, Zhejiang University of Technology","department":""},{"first_name":"zhiyu","middle_name":"","last_name":"chen","name_suffix":"","institution":"Computer Science and Technology","department":""},{"first_name":"Guojiang","middle_name":"","last_name":"Shen","name_suffix":"","institution":"Zhejiang University of Technology","department":""},{"first_name":"Xiangjie","middle_name":"","last_name":"Kong","name_suffix":"","institution":"Zhejiang University of Technology","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2024-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/24380/galley/13977/download/"},{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/24380/galley/21109/download/"}]}