位置社交网络中基于评论文本的兴趣点推荐

摘要

随着位置社交网络(Location-Based Social Networks,LBSN)的快速发展,兴趣点(Point-Of-Interest,POI)推荐对于用户和商家愈发重要.目前基于社交网络的兴趣点推荐算法主要利用用户的历史签到数据和社交网络数据来提升推荐质量,但忽略了利用兴趣点的评论文本数据;并且LBSN中的数据经常会存在部分信息缺失的情况,对兴趣点推荐算法而言如何保证鲁棒性是一个巨大的挑战.为此,提出了一种新的用户兴趣点推荐模型,称其为SoGeoCom模型.该模型融合了用户社交网络数据、地理位置数据以及兴趣点的评论文本数据这3个因素来进行兴趣点推荐.基于来自Yelp的真实数据集的实验结果表明,与其他主流的兴趣点推荐算法相比,SoGeoCom模型能够提高准确率和召回率,并且具有良好的鲁棒性,获得了更好的推荐效果.%With the rapid development of the location-based social networks(LBSN),the point-of-interest(POI) recommendation is becoming more and more important to users and businesses.At present,the recommendation algorithm based on social network mainly uses the user's historical data and social network data to improve the quality of recommendation,but ignores the POI's comment text data.And the data in LBSN often have some missing information,how to guarantee robustness is a huge challenge for the point-of-interest recommendation algorithms.To this end,this paper proposed a new model of point-of-interest recommendation,called SoGeoCom model.The model combines the user's social network data,geographic location data and the POI's comment text data to carry on the POI recommendation.Experimental results based on real data set from Yelp show that,compared with other mainstream POI recommendation models,the SoGeoCom model can improve the precision and recall rate,have good robustness,and get a better recommendation effect.