基于地理邻近性的自编码器在地点推荐中的应用

个性化地点推荐系统对于基于位置的社交网络（Locationbased Social Networks, LBSNs）的发展至关重要。它不仅能够帮助用户挖掘新的地点，同时也有利于服务商更好地提供个性化服务。现存关于这方面的研究，将所有的地点同等看待。但是在不同类别中，签到频率的数据规模却不可同等看待。本文基于TFIDF理论将签到频率转换成基于类别的偏好数据，提出一个基于地理邻近性的深度自编码器模型，利用签到数据中的地理信息构造推荐系统。在LBSNs真实数据集上进行实验分析，结果表明相对于对比算法，本文模型的实验结果更好，基于地理邻近性的深度自编码器模型适用于地点推荐任务。

GeoDAE for Pointofinterest Recommendation

 Personalized pointofinterest (POI) recommendation is crucial to the development of locationbased social networks (LBSNs). It not only helps users explore new places but also enables thirdparty services to better provide service. Previous studies on this topic treat all POIs as equal. Learning preferences within category makes sense, but the scale in which the frequency of checkins operates is not comparable across categories. In this paper, we transform the checkin frequency into categorybased preference according to TFIDF theory. And then we propose a GeoDAE model for the geographical proximity among POIs. The experimental results based on datasets from realworld LBSNs show that the proposed model achieves better performance than other stateoftheart methods, and the proposed model is a better alternative for POI recommendation.