计算机科学 ›› 2019, Vol. 46 ›› Issue (10): 77-83.doi: 10.11896/jsjkx.180901757
陈炯1, 张虎2,3, 曹付元2,3
CHEN Jiong1, ZHANG Hu2,3, CAO Fu-yuan2,3
摘要： 兴趣点(Point-of-Interest,POI)推荐是为用户推荐可能感兴趣的地理位置的一项任务,是基于位置社交网络(Location-Based Social Networks,LBSN)服务中的重要研究内容。针对目前POI推荐准确率较低、推荐结果缺乏个性化、情感倾向因素融入差等问题,在综合分析兴趣点的地理位置、分类偏好、流行度、社交与情感倾向等相关影响因素的基础上,提出了融合多因素的兴趣点协同推荐模型(GCSR)。首先,根据POI地理位置数据计算地理相关分数;其次,根据用户的类别偏好,结合POI流行度定义分类偏好分数;然后,根据社交关系计算用户之间的社交关系强度,通过挖掘评论文本计算用户的情感倾向分数,并将二者与协同过滤推荐技术有效结合,从而得到社交情感分数;最后,将地理相关分数、分类偏好分数与社交情感分数有效融合,向用户推荐Top-N兴趣点。在Foursquare真实签到数据集上进行的多组对比实验显示,与基线模型中最好的JRA相比,GCSR模型能够获得更好的推荐效果,准确率和召回率平均提高了1.7%和0.6%。
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