With the widespread use of mobile phones,users can share their location and activity anytime,anywhere,as a form of check-in data.These data reflect user features.Long-term stability and a set of user-shared features c...With the widespread use of mobile phones,users can share their location and activity anytime,anywhere,as a form of check-in data.These data reflect user features.Long-term stability and a set of user-shared features can be abstracted as user roles.This role is closely related to the users’social background,occupation,and living habits.This study makes four main contributions to the literature.First,user feature models from different views for each user are constructed from the analysis of the check-in data.Second,the K-means algorithm is used to discover user roles from user features.Third,a reinforcement learning algorithm is proposed to strengthen the clustering effect of user roles and improve the stability of the clustering result.Finally,experiments are used to verify the validity of the method.The results show that the method can improve the effect of clustering by 1.5∼2 times,and improve the stability of the cluster results about 2∼3 times of the original.This method is the first time to apply reinforcement learning to the optimization of user roles in mobile applications,which enhances the clustering effect and improves the stability of the automatic method when discovering user roles.展开更多
With the development of web 2.0, more and more social community applications appeared. The classical type of this kind of application is blog and facebook. The most important feature of these applications is that it i...With the development of web 2.0, more and more social community applications appeared. The classical type of this kind of application is blog and facebook. The most important feature of these applications is that it is a self-media and users can post their own ideas in Internet. By using these social community applications, a big social network is formed. To study the feature of social network, it is important to mine the individual information at the beginning. In this paper, we propose a User Role based method to mine the relation between the user and object thing. First, we extract the User Role from the semantic dictionary Wordnet. Then, the feature of User Role is also mined by considering the hypemymy and hyponymy relation. Finally, we can use these features to deduce the User Role. In our experiments, we use a big corpus from TREC 2006 to test the mining performance. The experiment results show that the User Role effectively explores the feature of user.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.U1504602.
文摘With the widespread use of mobile phones,users can share their location and activity anytime,anywhere,as a form of check-in data.These data reflect user features.Long-term stability and a set of user-shared features can be abstracted as user roles.This role is closely related to the users’social background,occupation,and living habits.This study makes four main contributions to the literature.First,user feature models from different views for each user are constructed from the analysis of the check-in data.Second,the K-means algorithm is used to discover user roles from user features.Third,a reinforcement learning algorithm is proposed to strengthen the clustering effect of user roles and improve the stability of the clustering result.Finally,experiments are used to verify the validity of the method.The results show that the method can improve the effect of clustering by 1.5∼2 times,and improve the stability of the cluster results about 2∼3 times of the original.This method is the first time to apply reinforcement learning to the optimization of user roles in mobile applications,which enhances the clustering effect and improves the stability of the automatic method when discovering user roles.
基金National Natural Science Foundations of China (No.60873179, No.60803078)Shenzhen Municipal Science and Technology Planning Program for Basic Research, China ( No. JC200903180630A)+1 种基金Research Fund for the Doctoral Program of Higher Education of China (No.20090121110032)Technology Research Program of Fujian Province of China (No.2006H0037)
文摘With the development of web 2.0, more and more social community applications appeared. The classical type of this kind of application is blog and facebook. The most important feature of these applications is that it is a self-media and users can post their own ideas in Internet. By using these social community applications, a big social network is formed. To study the feature of social network, it is important to mine the individual information at the beginning. In this paper, we propose a User Role based method to mine the relation between the user and object thing. First, we extract the User Role from the semantic dictionary Wordnet. Then, the feature of User Role is also mined by considering the hypemymy and hyponymy relation. Finally, we can use these features to deduce the User Role. In our experiments, we use a big corpus from TREC 2006 to test the mining performance. The experiment results show that the User Role effectively explores the feature of user.
