基于CiteSpace知识图谱分析方法,系统梳理了发表于Web of Science和中国知网数据库中的703篇外文文献与1036篇中文文献,围绕关键词词频、聚类与突现特征,系统呈现了要素市场扭曲的研究概况。在此基础上,依据生产要素的基本分类,从资本...基于CiteSpace知识图谱分析方法,系统梳理了发表于Web of Science和中国知网数据库中的703篇外文文献与1036篇中文文献,围绕关键词词频、聚类与突现特征,系统呈现了要素市场扭曲的研究概况。在此基础上,依据生产要素的基本分类,从资本、劳动力、土地以及数据四个层面归纳并评述了当前研究的主流。最后,结合现有研究的不足与新时代背景下的现实需求,提出未来研究的五个重点方向,包括:聚焦中国情境下的要素市场扭曲、深化数据要素市场研究、分析不同要素市场扭曲的相互作用、开展跨学科交叉研究、探讨新兴技术驱动下的要素市场扭曲,旨在深化对该领域的全面认识,并为后续相关研究提供理论参考与思路借鉴。展开更多
The rapid development of location-based social networks(LBSNs) provides people with an opportunity of better understanding their mobility behavior which enables them to decide their next location.For example,it can he...The rapid development of location-based social networks(LBSNs) provides people with an opportunity of better understanding their mobility behavior which enables them to decide their next location.For example,it can help travelers to choose where to go next,or recommend salesmen the most potential places to deliver advertisements or sell products.In this paper,a method for recommending points of interest(POIs)is proposed based on a collaborative tensor factorization(CTF)technique.Firstly,a generalized objective function is constructed for collaboratively factorizing a tensor with several feature matrices.Secondly,a 3-mode tensor is used to model all users' check-in behaviors,and three feature matrices are extracted to characterize the time distribution,category distribution and POI correlation,respectively.Thirdly,each user's preference to a POI at a specific time can be estimated by using CTF.In order to further improve the recommendation accuracy,PCTF(Partitionbased CTF) is proposed to fill the missing entries of a tensor after clustering its every mode.Experiments on a real checkin database show that the proposed method can provide more accurate location recommendation.展开更多
文摘基于CiteSpace知识图谱分析方法,系统梳理了发表于Web of Science和中国知网数据库中的703篇外文文献与1036篇中文文献,围绕关键词词频、聚类与突现特征,系统呈现了要素市场扭曲的研究概况。在此基础上,依据生产要素的基本分类,从资本、劳动力、土地以及数据四个层面归纳并评述了当前研究的主流。最后,结合现有研究的不足与新时代背景下的现实需求,提出未来研究的五个重点方向,包括:聚焦中国情境下的要素市场扭曲、深化数据要素市场研究、分析不同要素市场扭曲的相互作用、开展跨学科交叉研究、探讨新兴技术驱动下的要素市场扭曲,旨在深化对该领域的全面认识,并为后续相关研究提供理论参考与思路借鉴。
基金supported in part by the National Nature Science Foundation of China(91218301,61572360)the Basic Research Projects of People's Public Security University of China(2016JKF01316)Shanghai Shuguang Program(15SG18)
文摘The rapid development of location-based social networks(LBSNs) provides people with an opportunity of better understanding their mobility behavior which enables them to decide their next location.For example,it can help travelers to choose where to go next,or recommend salesmen the most potential places to deliver advertisements or sell products.In this paper,a method for recommending points of interest(POIs)is proposed based on a collaborative tensor factorization(CTF)technique.Firstly,a generalized objective function is constructed for collaboratively factorizing a tensor with several feature matrices.Secondly,a 3-mode tensor is used to model all users' check-in behaviors,and three feature matrices are extracted to characterize the time distribution,category distribution and POI correlation,respectively.Thirdly,each user's preference to a POI at a specific time can be estimated by using CTF.In order to further improve the recommendation accuracy,PCTF(Partitionbased CTF) is proposed to fill the missing entries of a tensor after clustering its every mode.Experiments on a real checkin database show that the proposed method can provide more accurate location recommendation.
