The rapid development of location-based social networks (LBSNs) has provided an unprecedented opportunity for better location-based services through Point-of-Interest (POI) recommendation.POI recommendation is a personalized,location-aware,and context depended recommendation.However,extreme sparsity of user-POI matrix creates a severe challenge.In this paper,we propose a context-aware probabilistic matrix factorization method called TGSC-PMF for POI recommendation,exploiting geographical information,text information,social information,categorical information and popularity information,incorporating these factors effectively.First,we exploit an aggregated Latent Dirichlet Allocation (LDA) model to learn the interest topics of users and infer the interest POIs by mining textual information associated with POIs and generate interest relevance score.Second,we propose a kernel estimation method with an adaptive bandwidth to model the geographical correlations and generate geographical relevance score.Third,we build social relevance through the power-law distribution of user social relations to generate social relevance score.Then,we model the categorical correlations which combine the category bias of users and the popularity of POIs into categorical relevance score.Further,we exploit probabilistic matrix factorization model (PMF) to integrate the interest,geographical,social and categorical relevance scores for POI recommendation.Finally,we implement experiments on a real LBSN check-in dataset.Experimental results show that TGSC-PMF achieves significantly superior recommendation quality compare to other state-of-the-art POI recommendation techniques.%随着基于位置社交网络(Location-Based Social Networks,LBSN)的快速发展,兴趣点(Point-of-Interest,POI)推荐为基于位置的服务提供了前所未有的机会.兴趣点推荐是一种基于上下文信息的位置感知的个性化推荐.然而用户-兴趣点矩阵的极端稀疏给兴趣点推荐的研究带来严峻挑战.为处理数据稀疏问题,文中利用兴趣点的地理、文本、社会、分类与流行度信息,并将这些因素进行有效地融合,提出一种上下文感知的概率矩阵分解兴趣点推荐算法,称为TGSC-PMF.首先利用潜在狄利克雷分配(Latent Dirichlet Allocation,LDA)模型挖掘兴趣点相关的文本信息学习用户的兴趣话题生成兴趣相关分数;其次提出一种自适应带宽核评估方法构建地理相关性生成地理相关分数;然后通过用户社会关系的幂律分布构建社会相关性生成社会相关分数;另外结合用户的分类偏好与兴趣点的流行度构建分类相关性生成分类相关分数,最后利用概率矩阵分解模型(Probabilistic Matrix Factorization,PMF),将兴趣、地理、社会、分类的相关分数进行有效地融合,从而生成推荐列表推荐给用户感兴趣的兴趣点.该文在一个真实LBSN签到数据集上进行实验,结果表明该算法相比其他先进的兴趣点推荐算法具有更好的推荐效果.
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