摘要
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.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2016年第4期752-763,共12页
Journal of Computer Research and Development
基金
国家"九七三"重点基础研究发展计划基金项目(2012CB719905)
国家自然科学基金青年项目(41201404)
中央高校基本科研业务费专项资金(2042015gf0009)~~
关键词
地点推荐
矩阵分解
社交关系
地理信息
评论文本
location recommendation
matrix factorization
social relationships
geographical information
review text