An Empirical Evaluation of Cross-City POI Recommendation on a Large-Scale Benchmark

[2608.27840] An Empirical Evaluation of Cross-City POI Recommendation on a Large-Scale Benchmark Skip to main content Search Submit Donate Log in Computer Science > Artificial Intelligence arXiv:2608.27840 (cs) [Submitted on 28 Aug 2026] Title: An Empirical Evaluation of Cross-City POI Recommendation on a Large-Scale Benchmark Authors: Peibo Li , Yang Song , Hao Xue , Maarten de Rijke , Flora D. Salim View PDF HTML (experimental) Abstract: Cross-city point-of-interest (POI) recommendation is crucial for navigating unfamiliar urban environments, yet its progress has historically been constrained by data limitations. Using the recently proposed large-scale benchmark Trip World, we empirically re-examine whether conclusions drawn on small prior benchmarks still hold under worldwide coverage, low home-destination region overlap, and large, semantically rich POI inventories. Our evaluation surfaces three bottlenecks of representative state-of-the-art methods: (1) hometown-aware models appear to rely more on destination-region priors than on user-specific preference transfer; (2) their accuracy-efficiency trade-off degrades at this scale, where the simplest model is among the strongest; and (3) existing mechanisms for integrating semantic metadata yield little benefit. We further include a diagnostic pilot on agentic methods adapted from next-POI recommendation, finding that naive adaptation trails a simple popularity prior even though the relevant semantic signal is present in the data. These results highlight the need for task-specific designs that support cross-city preference transfer, semantic grounding, and scalable reasoning over unseen destination inventories. Subjects: Artificial Intelligence (cs.AI) ; Information Retrieval (cs.IR) Cite as: arXiv:2608.27840 [cs.AI] (or arXiv:2608.27840v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.27840 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1145/3841645.3843403 Focus to learn more DOI(s) linking to related resources Submission history From: Peibo Li [ view email ] [v1] Fri, 28 Aug 2026 02:16:33 UTC (72 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.AI < prev | next > new | recent | 2026-08 Change to browse by: cs cs.IR References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

Related documents

• An Empirical Evaluation of Cross-City POI Recommendation on a Large-Scale Benchmark#779751
• National Travel Survey, 1995-2001#376567
• TrajPrism: A Multi-Task Benchmark for Language-Grounded Urban Trajectory Understanding#175305
• Methodology for Identifying Trail Deserts: Exploring Modal Access to Trails through Demographic and Land Use Factors#390783
• Inferring Behavioral Regimes in Urban Mobility via Spatio-Temporal Optimal Transport#374052