Microsoft, Mountain View, CA, United States of America.
North Carolina State University, Raleigh, NC, United States of America.
PLoS One. 2021 Aug 5;16(8):e0255685. doi: 10.1371/journal.pone.0255685. eCollection 2021.
Geographical characteristics have been proven to be effective in improving the quality of point-of-interest (POI) recommendation. However, existing works on POI recommendation focus on cost (time or money) of travel for a user. An important geographical aspect that has not been studied adequately is the neighborhood effect, which captures a user's POI visiting behavior based on the user's preference not only to a POI, but also to the POI's neighborhood. To provide an interpretable framework to fully study the neighborhood effect, first, we develop different sets of insightful features, representing different aspects of neighborhood effect. We employ a Yelp data set to evaluate how different aspects of the neighborhood effect affect a user's POI visiting behavior. Second, we propose a deep learning-based recommendation framework that exploits the neighborhood effect. Experimental results show that our approach is more effective than two state-of-the-art matrix factorization-based POI recommendation techniques.
地理位置特征已被证明可以有效提高兴趣点（POI）推荐的质量。然而，现有的 POI 推荐工作主要关注用户旅行的成本（时间或金钱）。一个尚未得到充分研究的重要地理方面是邻里效应，它基于用户对 POI 的偏好以及对 POI 周边地区的偏好来捕捉用户的 POI 访问行为。为了提供一个可解释的框架来充分研究邻里效应，首先，我们开发了不同的有见地的特征集，代表了邻里效应的不同方面。我们使用 Yelp 数据集来评估邻里效应的不同方面如何影响用户的 POI 访问行为。其次，我们提出了一种基于深度学习的推荐框架，利用了邻里效应。实验结果表明，我们的方法比两种最先进的基于矩阵分解的 POI 推荐技术更有效。
