Point-of-interest (POI) recommendation has great significance in helping users find favorite places from a large number of candidate venues. One challenging in POI recommendation is to effectively exploit geographical information since users usually care about the physical distance to the recommended POIs. Though spatial relevance has been widely considered in recent recommendation methods, it is modeled only from the POI perspective, failing to capture user personalized preference to spatial distance. Moreover, these methods suffer from a diversity-deficiency problem since they are often based on collaborative filtering which always favors popular POIs. To overcome these problems, we propose in this article a personalized geographical influence modeling method called PGIM, which jointly learns users’ geographical preference and diversity preference for POI recommendation. Specifically, we model geographical preference from three aspects: user global tolerance, user local tolerance, and spatial distance. We also extract user diversity preference from interactions among users for diversity-promoting recommendation. Experimental results on three real-world datasets demonstrate the superiority of PGIM.

中文翻译：

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兴趣点推荐的个性化地理影响建模

兴趣点（POI）推荐对于帮助用户从大量候选场地中找到喜欢的地点具有重要意义。POI 推荐的一个挑战是有效利用地理信息，因为用户通常关心到推荐 POI 的物理距离。尽管在最近的推荐方法中已经广泛考虑了空间相关性，但它仅从 POI 的角度进行建模，未能捕捉到用户对空间距离的个性化偏好。此外，这些方法存在多样性不足的问题，因为它们通常基于协作过滤，而协作过滤总是有利于流行的 POI。为了克服这些问题，我们在本文中提出了一种称为 PGIM 的个性化地理影响建模方法，联合学习用户对 POI 推荐的地理偏好和多样性偏好。具体来说，我们从三个方面对地理偏好进行建模：用户全局容忍度、用户局部容忍度和空间距离。我们还从用户之间的交互中提取用户多样性偏好，用于促进多样性的推荐。三个真实世界数据集的实验结果证明了 PGIM 的优越性。