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Point-of-interest recommendation in location-based social networks with personalized geo-social influence

机译：具有个性化地缘社会影响力的基于位置的社交网络中的兴趣点推荐

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摘要

Point-of-interest (POI) recommendation is a popular topic on location-based social networks (LBSNs). Geographical proximity, known as a unique feature of LBSNs, significantly affects user check-in behavior. However, most of prior studies characterize the geographical influence based on a universal or personalized distribution of geographic distance, leading to unsatisfactory recommendation results. In this paper, the personalized geographical influence in a two-dimensional geographical space is modeled using the data field method, and we propose a semi-supervised probabilistic model based on a factor graph model to integrate different factors such as the geographical influence. Moreover, a distributed learning algorithm is used to scale up our method to large-scale data sets. Experimental results based on the data sets from Foursquare and Gowalla show that our method outperforms other competing POI recommendation techniques.
机译：兴趣点（POI）推荐是基于位置的社交网络（LBSN）上的热门话题。地理邻近度被称为LBSN的独特功能，它会严重影响用户的签到行为。但是，大多数先前的研究基于地理距离的普遍或个性化分布来描述地理影响，从而导致推荐结果不令人满意。本文利用数据域方法对二维地理空间中的个性化地理影响力进行了建模，提出了一种基于因子图模型的半监督概率模型，以整合地理影响力等不同因素。此外，使用分布式学习算法将我们的方法扩展到大规模数据集。基于来自Foursquare和Gowalla的数据集的实验结果表明，我们的方法优于其他竞争性POI推荐技术。

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