典型文献
Exploiting user behavior learning for personalized trajectory recommendations
文献摘要：
With increasing popularity of mobile devices and flourish of social networks,a large number of trajectory data is accumulated.Trajectory data contains a wealth of information,including spatiality,time series,and other external descriptive attributes(i.e.,travelling mode,activities,etc.).Trajectory recommendation is especially important to users for finding the routes meeting the user's travel needs quickly.Most existing trajectory recommendation works return the same route to different users given an origin and a destination.However,the users'behavior preferences can be learned from users'his-torical multi-attributes trajectories.In this paper,we propose two novel personalized trajectory recommendation methods,i.e.,user behavior probability learning based on matrix decom-position and user behavior probability learning based on Kernel density estimation.We transform the route recommendation problem to a shortest path problem employing Bayesian proba-bility model.Combining the user input(i.e.,an origin and a destination),the trajectory query is performed on a behavior graph based on the learned behavior probability automatically.Finally,a series of experiments on two real datasets validate the effectiveness of our proposed methods.
文献关键词：
中图分类号：
作者姓名：
Xiao PAN;Lei WU;Fenjie LONG;Ang MA
作者机构：
School of Economics and Management,Shijiazhuang Tiedao University,Shijiazhuang 050043,China
文献出处：
引用格式：
[1]Xiao PAN;Lei WU;Fenjie LONG;Ang MA-.Exploiting user behavior learning for personalized trajectory recommendations)[J].计算机科学前沿,2022(03):137-148
A类：
flourish,spatiality
B类：
Exploiting,behavior,learning,personalized,trajectory,recommendations,With,increasing,popularity,mobile,devices,social,networks,large,number,accumulated,Trajectory,contains,wealth,information,including,series,other,external,descriptive,attributes,travelling,activities,etc,especially,important,users,finding,routes,meeting,needs,quickly,Most,existing,return,same,different,given,origin,destination,However,preferences,can,learned,from,torical,multi,trajectories,In,this,paper,novel,methods,probability,matrix,decom,position,Kernel,density,estimation,We,transform,problem,shortest,path,employing,Bayesian,model,Combining,input,query,performed,graph,automatically,Finally,experiments,real,datasets,validate,effectiveness,proposed
AB值：
0.53507
相似文献
机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
