LBSNs中的群体行程推荐方法

doi:10.11896/j.issn.1002-137X.2017.06.033

摘要/Abstract

摘要： 随着GPS设备(如智能手机、GPS导航仪、GPS记录仪等)的广泛应用,其产生的位置信息也越来越多。基于位置的社交网络(Location-Based Social Networks,LBSNs)推荐系统受到了更多的关注。旅游行程推荐是LBSNs中非常热门的研究课题之一,但是现有研究主要侧重向单个用户推荐旅游行程,缺乏向群体推荐行程的工作。因此提出了一种LBSNs中的群体行程推荐方法。该方法首先根据用户的签到记录,使用K-means和谱聚类方法挖掘用户群体及其偏好；然后综合考虑群体对行程的时间和价格的约束,设计了行程推荐算法向群体用户推荐符合其偏好的旅游行程；最后,使用新浪微博用户的真实签到记录进行实验分析,结果表明所提出的群体行程推荐方法具有良好效果。

关键词: 群体,旅游行程,LBSNs,谱聚类,K-means,推荐系统

Abstract: With the widespread adoption of GPS-enabled devices,such as smartphone,GPS navigation device,GPS logger,etc.,more and more location information is collected.Recommender systems for location-based social networks (LBSNs) have received more attention.The research on recommending a trip to a group is a hot topic,but most related works mainly focus on recommending trip to a user and lack in recommending trip to a group.Therefore,this paper proposed a trip recommender method for a group in LBSNs.First,according to user’s check-ins,the proposed method uses K-means and spectral clustering to mine groups who have a great many same check-ins.Then,group’s preference is obtained based on their common check-ins.At last, combining group’s time and cost constraint,a trip recommender algorithm is designed to recommend trip which satisfies group’s preference to a group.This paper conducted experiments with users’ real check-ins of Sina weibo.The experimental results show that the proposed method in this paper to recommend trips to a group achieves good effects.

Key words: Group,Travel trip,LBSNs,Spectral clustering,K-means,Recommender system

李效伦,丁志军. LBSNs中的群体行程推荐方法[J]. 计算机科学, 2017, 44(6): 199-205. https://doi.org/10.11896/j.issn.1002-137X.2017.06.033

LI Xiao-lun and DING Zhi-jun. Group Travel Trip Recommendation Method in LBSNs[J]. Computer Science, 2017, 44(6): 199-205. https://doi.org/10.11896/j.issn.1002-137X.2017.06.033

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