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An Empirical Evaluation of Cross-City POI Recommendation on a Large-Scale Benchmark
AI InsightThis study re-evaluates cross-city POI recommendation on the Trip World large-scale benchmark, revealing bottlenecks such as reliance on destination-region priors rather than user preference transfer and degraded accuracy-efficiency trade-offs. Compared to prior small-benchmark conclusions, this means model advantages do not extrapolate directly, and the simplest baseline performs strongly, signaling a need for algorithm design suited to global scale and low region overlap.Key TakeawayLarge-scale benchmark overturns small-scale conclusions; simple baseline outperforms complex models.Why It MattersRecalibrates the perceived effectiveness of POI recommendation methods in large-scale, low-overlap scenarios, preventing over-extrapolation and providing a more reliable benchmark for future evaluation.Who's Affected- AI ResearchersNeed to reassess assumptions and evaluation methods for cross-city recommendation, avoiding misleading large-model design from small-data conclusions.
- Recommendation EngineersMust re-trade-off accuracy and efficiency at scale; simple baselines can serve as strong references.
- Map Service CompaniesMay prioritize lightweight models to cut computation costs and reduce reliance on complex preference transfer modules.
What's NextWatch for new models or training strategies targeting large-scale cross-city recommendation, and subsequent adoption or extension of the Trip World benchmark.Importance 65/100