一种融合情景和评论信息的位置社交网络兴趣点推荐模型

A Synthetic Recommendation Model for Point-of-Interest on Location-Based Social Networks:Exploiting Contextual Information and Review

随着位置社交网络(location-based social network,LBSN)的快速增长,兴趣点(point-ofinterest,POI)推荐已经成为一种帮助人们发现有趣位置的重要方式.现有的研究工作主要是利用用户签到的历史数据及其情景信息(如地理信息、社交关系)来提高推荐质量,而忽视了利用兴趣点相关的评论信息.但是,现实中用户在LBSN中只对少数兴趣点进行签到,使得用户签到历史数据及其情景信息极其稀疏,这对兴趣点推荐来说是一个巨大的挑战.为此,提出了一种新的兴趣点推荐模型,称为GeoSoRev模型.该模型在已有的基于矩阵分解的经典推荐模型的基础上,融合关于兴趣点的评论信息、用户社交关联和地理信息这3个因素进行兴趣点推荐.基于2个来自Foursquare的真实数据集的实验结果表明,与其他主流的兴趣点推荐模型相比,GeoSoRev模型在准确率和召回率等多项评价指标上都取得了显著的提高.

With the rapid growth of location-based social network（LBSN）,point-of-interest（POI）recommendation has become an important mean to help people discover attractive locations.However,most of existing models of POI recommendation on LBSNs improve recommendation quality by exploiting the user check-in history behavior and contextual information（e.g.,geographical information and social correlations）,and they tend to ignore the review texts information accompanied with rating information for recommender models.While in reality,users only check in a few POIs in LBSN,which makes the user-POIs check-in history records and contextual information highly sparse,and causes a big challenge for POIs recommendations.To tackle this challenge,a novel POIs recommendation model called GeoSoRev is proposed in this paper,which combines users＇ preference to a POI with geographical information,social correlations and reviews text on the basis of the classic recommendation model based on matrix factorization.Experimental results on two real-world datasets collected from Foursquare show that GeoSoRev achieves significantly superior precision and recalling rates compared with other state-of-the-art POIs recommendation models.