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 lack of negative samples and the complexities of check-in contexts limit their effectiveness significantly. This paper focuses on the problem of context-specific POI recommendation based on the check-in behaviors recorded by Location-Based Social Network (LBSN) services, which aims at recommending a list of POIs for a user to visit at a given context (such as time and weather). Specifically, a bidirectional influence correlativity metric is proposed to measure the semantic feature of user check-in behavior, and a contextual smoothing method to effectively alleviate the problem of data sparsity. In addition, the check-in probability is computed based on the geographical distance between the user’s home and the POI. Furthermore, to handle the problem of no negative feedback in LBSN, a weighted random sampling method is proposed based on contextual popularity. Finally, the recommendation results is obtained by utilizing Factorization Machine with Bayesian Personalized Ranking (BPR) loss. Experiments on a real dataset collected from Foursquare show that the proposed approach has better performance than others.

中文翻译：

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基于流行度加权随机抽样和分解机的特定于上下文的兴趣点推荐

兴趣点（POI）推荐不仅可以帮助用户找到自己喜欢的地方，还可以帮助企业吸引潜在客户。最近的研究已经提出了许多针对POI建议的方法。但是，缺少否定样本以及签入环境的复杂性极大地限制了其有效性。本文基于基于位置的社交网络（LBSN）服务记录的签入行为，针对特定于上下文的POI推荐问题，该问题旨在为用户推荐在给定上下文中访问的POI列表（例如视时间和天气而定）。具体而言，提出了一种双向影响相关性度量来度量用户签到行为的语义特征，并提出一种上下文平滑方法来有效缓解数据稀疏性问题。此外，根据用户房屋和POI之间的地理距离计算签入概率。此外，为了解决LBSN中没有负反馈的问题，提出了一种基于上下文流行度的加权随机抽样方法。最后，利用具有贝叶斯个性化排名（BPR）损失的分解机获得推荐结果。在从Foursquare收集的真实数据集上进行的实验表明，该方法比其他方法具有更好的性能。通过利用具有贝叶斯个性化排名（BPR）损失的因数分解机获得推荐结果。在从Foursquare收集的真实数据集上进行的实验表明，该方法比其他方法具有更好的性能。通过利用具有贝叶斯个性化排名（BPR）损失的因数分解机获得推荐结果。在从Foursquare收集的真实数据集上进行的实验表明，该方法比其他方法具有更好的性能。