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Learning to Recommend Point-of-Interest with the Weighted Bayesian Personalized Ranking Method in LBSNs

机译：学习推荐LBSN中的加权贝叶斯个性化排序方法的兴趣点

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Point-of-interest (POI) recommendation has been well studied in recent years. However, most of the existing methods focus on the recommendation scenarios where users can provide explicit feedback. In most cases, however, the feedback is not explicit, but implicit. For example, we can only get a user’s check-in behaviors from the history of what POIs she/he has visited, but never know how much she/he likes and why she/he does not like them. Recently, some researchers have noticed this problem and began to learn the user preferences from the partial order of POIs. However, these works give equal weight to each POI pair and cannot distinguish the contributions from different POI pairs. Intuitively, for the two POIs in a POI pair, the larger the frequency difference of being visited and the farther the geographical distance between them, the higher the contribution of this POI pair to the ranking function. Based on the above observations, we propose a weighted ranking method for POI recommendation. Specifically, we first introduce a Bayesian personalized ranking criterion designed for implicit feedback to POI recommendation. To fully utilize the partial order of POIs, we then treat the cost function in a weighted way, that is give each POI pair a different weight according to their frequency of being visited and the geographical distance between them. Data analysis and experimental results on two real-world datasets demonstrate the existence of user preference on different POI pairs and the effectiveness of our weighted ranking method.
机译：近年来，对兴趣点（POI）推荐进行了深入研究。但是，大多数现有方法侧重于用户可以提供明确反馈的推荐方案。但是，在大多数情况下，反馈不是显式的，而是隐式的。例如，我们只能从用户访问过的POI的历史记录中获取用户的签到行为，但不知道她/他喜欢多少以及为什么他/他不喜欢它们。最近，一些研究人员注意到了这个问题，并开始从POI的偏序中学习用户的偏好。但是，这些工作对每个POI对具有同等的权重，无法区分不同POI对的贡献。直观地，对于一个POI对中的两个POI，被访问的频率差越大，并且它们之间的地理距离越远，则该POI对排序功能的贡献就越大。基于以上观察，我们提出了一种针对POI推荐的加权排序方法。具体而言，我们首先介绍一种贝叶斯个性化排名标准，旨在对POI建议进行隐式反馈。为了充分利用POI的偏序，我们然后对加权函数进行加权处理，即根据每个POI对的访问频率和它们之间的地理距离为它们赋予不同的权重。在两个真实数据集上的数据分析和实验结果证明了不同POI对上用户偏好的存在以及加权加权方法的有效性。

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