Location recommendation plays a vital role in improving users' travel experience. The timestamp of the POI to be predicted is of great significance, since a user will go to different places at different times. However, most existing methods either do not use this kind of temporal information, or just implicitly fuse it with other contextual information. In this paper, we revisit the problem of location recommendation and point out that explicitly modeling temporal information is a great help when the model needs to predict not only the next location but also further locations. In addition, state-of-the-art methods do not make effective use of geographic information and suffer from the hard boundary problem when encoding geographic information by gridding. To this end, a Temporal Prompt-based and Geography-aware (TPG) framework is proposed. The temporal prompt is firstly designed to incorporate temporal information of any further check-in. A shifted window mechanism is then devised to augment geographic data for addressing the hard boundary problem. Via extensive comparisons with existing methods and ablation studies on five real-world datasets, we demonstrate the effectiveness and superiority of the proposed method under various settings. Most importantly, our proposed model has the superior ability of interval prediction. In particular, the model can predict the location that a user wants to go to at a certain time while the most recent check-in behavioral data is masked, or it can predict specific future check-in (not just the next one) at a given timestamp.
RoTE: Coarse-to-Fine Multi-Level Rotary Time Embedding for Sequential Recommendation
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Huazhong University of Science and Technology（华中科技大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）
MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation Systems
Arizona State University（亚利桑那州立大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；MIT（麻省理工学院）；Harvard University（哈佛大学）；Mercari, Inc.（Mercari公司）
Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential Recommendation
University of Science and Technology of China（中国科学技术大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；School of Artificial Intelligence and Data Science, University of Science and Technology of China（中国科学技术大学人工智能与数据科学学院）；Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）人工智能学域）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程学系）
