融合相似度和地理信息的兴趣点推荐
    点此下载全文
引用本文:郭晨睿1,李平1,2,郭苗苗3.融合相似度和地理信息的兴趣点推荐[J].计算技术与自动化,2019,(3):67-73
摘要点击次数: 71
全文下载次数: 0
作者单位
郭晨睿1,李平1,2,郭苗苗3 (1. 长沙理工大学 计算机与通信工程学院湖南 长沙 410114 2. 智能交通大数据处理湖南省重点实验室湖南 长沙 4100143. 长沙大学图书馆湖南 长沙 410022) 
中文摘要:兴趣点推荐是一种基于上下文信息的位置感知的个性化推荐。由于用户签到行为具有高稀疏性,为兴趣点推荐的精确度带来了很大的挑战。针对该问题,提出了一种融合相似度和地理信息的兴趣点推荐模型,称为SIGFM。首先利用潜在迪利克雷分配(Laten Dirichlet Allocation,LDA)模型挖掘用户相关兴趣特征并进行相似性度量,利用Louvain Community Detection(LCD)算法与用户签到数据进行相似性度量,使两种相似度相融合;然后使用地理信息获取用户的签到特征;最后将融合相似度和地理信息结合到一起获得一个新的模型。在真实数据集上的实验结果表明,SIGFM模型有效解决了数据稀疏性与冷启动问题,优于其他POIs的推荐算法。
中文关键词:潜在狄利克雷分布  Louvain社区发现  兴趣点推荐  地理信息  相似度
 
Points-of-interest Recommendation with Similarity and Geographic Information
Abstract:Point-of-interests(POIs) recommendation is a personalized recommendation based on location-aware with context information.Owing to behavior of check-in from the users is highly sparse,which poses the challenge to the accuracy of the POIs recommendation.In order to solve this problem,this paper propose a new POIs recommendation called Similarity Integration Geography Fusing Model(SIGFM).Firstly,we exploit an aggregated Latent Dirichlet Allocation(LDA) model to learn the interest feature from the users,and then puts the interest feature into similarity measurement.Also,we use the Louvain Community Detection(LCD) and check-in data from the users to calculate the similarity.The similarity measurement utilizing both methods finally merge into the one.Then,a geographical influence measurement is employed to capture the check-in characteristice from the users. Finally,geographical informationin conjunction with the similarity forms the new model.Experimental results show that SIGFM can effectively mitigatethe sparse-data usually suffered and the cold-start suffer to outperforms other methods.
keywords:latent dirichlet allocation(LDA)  Louvain community detection(LCD)  point-of-interests(POIs)recommendation  geographic information  similarity
查看全文   查看/发表评论   下载pdf阅读器