By suggesting new visiting places, point-of-interest (POI) recommendation not only assists users to find their preferred places, but also helps businesses to attract potential customers. Recent studies have proposed many approaches to the POI recommendation. However, the data sparsity and complexity of user check-in behavior still pose big challenges to accurate personalized POI recommendation. To tackle these problems, in this paper, we propose a POI recommendation model named HeteGeoRankRec based on user contextual behavior semantics. First, we employ the meta-path of heterogeneous information network (HIN) to represent the complex semantic relationship among users and POIs. Second, we introduce different context constraints (such as time and weather) into the meta-path, to reveal the fine-grained user behavioral features. Afterwards, we propose a weighted matrix factorization model which considers the influence of geographical distance through the user–POI semantic correlativity matrices generated by multiple meta-paths. Finally, we present a fusion method based on learning to rank, which unifies the recommendation results of different meta-paths as the final user preference. The experiments on the real data collected from Foursquare demonstrate that HeteGeoRankRec has the better performance than the state-of-the-art baselines.

中文翻译：

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基于用户上下文行为语义的兴趣点推荐

通过推荐新的访问地点，兴趣点（POI）推荐不仅可以帮助用户找到他们喜欢的地方，还可以帮助企业吸引潜在客户。最近的研究提出了许多 POI 建议的方法。然而，用户签到行为的数据稀疏性和复杂性仍然对准确的个性化 POI 推荐提出了很大的挑战。为了解决这些问题，在本文中，我们提出了一种基于用户上下文行为语义的 POI 推荐模型 HeteGeoRankRec。首先，我们使用异构信息网络（HIN）的元路径来表示用户和 POI 之间的复杂语义关系。其次，我们将不同的上下文约束（例如时间和天气）引入元路径，以揭示细粒度的用户行为特征。然后，我们提出了一种加权矩阵分解模型，该模型通过多个元路径生成的用户-兴趣点语义相关矩阵考虑地理距离的影响。最后，我们提出了一种基于学习排序的融合方法，将不同元路径的推荐结果统一为最终的用户偏好。对从 Foursquare 收集的真实数据进行的实验表明，HeteGeoRankRec 具有比最先进的基线更好的性能。