Academic Journal
A user trajectory simulation framework for next POI recommendation with uncertain check-ins.
| Τίτλος: | A user trajectory simulation framework for next POI recommendation with uncertain check-ins. |
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| Συγγραφείς: | Li C; School of Artificial Intelligence, Yanshan University, Qinhuangdao, 066000, China. Electronic address: lichen36211@gmail.com., Huang G; School of Artificial Intelligence, Yanshan University, Qinhuangdao, 066000, China. Electronic address: gyhuang103@outlook.com., Feng S; School of Computer Science, Wuhan University, Wuhan, 430072, China. Electronic address: victor_fengss@whu.edu.cn., Sun Z; Pillar of Information Systems Technology and Design, Singapore University of Technology and Design, 487372, Singapore. Electronic address: sunzhuntu@gmail.com. |
| Πηγή: | Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2026 Aug; Vol. 200, pp. 108831. Date of Electronic Publication: 2026 Mar 12. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: New York : Pergamon Press, [c1988- |
| Ιατρικοί όροι (MeSH): | Computer Simulation*, Uncertainty ; Monte Carlo Method ; Humans ; Algorithms ; Reward |
| Περίληψη: | Next point-of-interest (POI) recommendation faces challenges from uncertain check-ins, especially within collective POIs (CPOIs)-venues like shopping malls containing multiple individual POIs (IPOIs). Due to unobserved user behaviors within CPOIs, existing methods treat CPOIs as proxies for their contained IPOIs, which limits next POI recommendation performance. To address this, we propose TraSim, a user Trajectory Simulation framework to simulate user behaviors within CPOIs based on Monte Carlo Tree Search, to approximate the potential trajectory within CPOIs, thus improving the recommendation accuracy under uncertain check-in scenarios. Specifically, to guide the simulation process, TraSim is equipped with a heuristic reward module that integrates (1) IPOI-level signals-temporal traits, transition patterns, and user personalized preferences across various activities, and (2) CPOI-level semantic constraints that narrow the simulation space based on the diversity of activities within each CPOI. Moreover, we construct a multi-candidate trajectory pool that retains diverse high-reward trajectories, enhancing simulation robustness. Furthermore, TraSim is model-agnostic and hyperparameter-free, allowing it to be a lightweight, plug-and-play module compatible with various baselines for next POI recommendation, thus providing a versatile and scalable solution. Extensive experiments on three real-world datasets (Calgary, Charlotte, and Phoenix) and nine competitive baselines demonstrate TraSim's effectiveness and efficiency, with average improvements of 49.1% in HR and 44.0% in MRR. (Copyright © 2026 Elsevier Ltd. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Monte carlo tree search; Next POI recommendation; Trajectory simulation; Uncertain check-ins |
| Entry Date(s): | Date Created: 20260320 Date Completed: 20260711 Latest Revision: 20260711 |
| Update Code: | 20260711 |
| DOI: | 10.1016/j.neunet.2026.108831 |
| PMID: | 41861774 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1879-2782 |
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| DOI: | 10.1016/j.neunet.2026.108831 |