新开放的地点也能预测，靠的是上下文和位置先验。
Forecasting Unseen Points of Interest Visits Using Context and Proximity Priors
- 先预测潜在地点的语义上下文，再结合位置临近性确定具体地点。
- 相比基线模型，准确率提升17%，且随新地点出现准确率下降更慢。
- 适合需要预测未见过的新场所的应用，如城市规划、推荐系统。
理解人类移动行为对人群管理、基于位置的推荐及疫情传播估计等应用至关重要。机器学习模型可通过分析用户历史访问记录，预测其未来可能访问的兴趣点（POI）。以往研究通常训练一个POI分类器，每个类别对应一个特定POI，但无法预测训练数据中未出现的新POI（如新开餐厅）。为此，我们提出一种新模型，能在新POI的上下文与用户兴趣匹配时进行预测。不同于直接预测具体POI的方法，本模型首先预测潜在未来POI的语义上下文，再结合基于邻近性的先验概率分布，推断出确切地点。在真实世界访问数据上的实验表明，该模型优于不考虑语义上下文的基线方法，准确率提升17%。值得注意的是，随着新POI不断引入，本模型的预测准确率下降更缓慢，表现出更强的鲁棒性。
原文摘要 · Abstract (English)
Understanding human mobility behavior is crucial for numerous applications, including crowd management, location-based recommendations, and the estimation of pandemic spread. Machine learning models can predict the Points of Interest (POIs) that individuals are likely to visit in the future by analyzing their historical visit patterns. Previous studies address this problem by learning a POI classifier, where each class corresponds to a POI. However, this limits their applicability to predict a new POI that was not in the training data, such as the opening of new restaurants. To address this challenge, we propose a model designed to predict a new POI outside the training data as long as its context is aligned with the user's interests. Unlike existing approaches that directly predict specific POIs, our model first forecasts the semantic context of potential future POIs, then combines this with a proximity-based prior probability distribution to determine the exact POI. Experimental results on real-world visit data demonstrate that our model outperforms baseline methods that do not account for semantic contexts, achieving a 17% improvement in accuracy. Notably, as new POIs are introduced over time, our model remains robust, exhibiting a lower decline rate in prediction accuracy compared to existing methods.
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