More and more accounts or devices are shared by multiple users in video applications,which makes it difficult to provide recommendation service.Existing recommendation schemes overlook multiuser sharing scenarios,and ...More and more accounts or devices are shared by multiple users in video applications,which makes it difficult to provide recommendation service.Existing recommendation schemes overlook multiuser sharing scenarios,and they cannot make effective use of the mixed information generated by multi-user when exploring users’potential interests.To solve these problems,this paper proposes an adaptive program recommendation system for multi-user sharing environment.Specifically,we first design an offline periodic identification module by building multi-user features and periodically predicting target user in future sessions,which can separate the profile of target user from mixed log records.Subsequently,an online recommendation module with adaptive timevarying exploration strategy is constructed by jointly using personal information and multi-user social information provided by identification module.On one hand,to learn the dynamic changes in user-interest,a time-varying linear upper confidence bound(LinUCB)based on personal information is designed.On the other hand,to reduce the risk of exploration,a timeinvariant LinUCB based on separated multi-user social information from one account/device is proposed to compute the quality scores of programs for each user,which is integrated into the time-varying LinUCB by cross-weighting strategy.Finally,experimental results validate the efficiency of the proposed scheme.展开更多
Smart learning systems provide relevant learning resources as a personalized bespoke package for learners based on their pedagogical needs and individual preferences.This paper introduces a learning style model to rep...Smart learning systems provide relevant learning resources as a personalized bespoke package for learners based on their pedagogical needs and individual preferences.This paper introduces a learning style model to represent features of online learners.It also presents an enhanced recommendation method named Adaptive Recommendation based on Online Learning Style(AROLS),which implements learning resource adaptation by mining learners’behavioral data.First,AROLS creates learner clusters according to their online learning styles.Second,it applies Collaborative Filtering(CF)and association rule mining to extract the preferences and behavioral patterns of each cluster.Finally,it generates a personalized recommendation set of variable size.A real-world dataset is employed for some experiments.Results show that our online learning style model is conducive to the learners’data mining,and AROLS evidently outperforms the traditional CF method.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.62277032,62231017,62071254)Education Scientific Planning Project of Jiangsu Province(Grant No.B/2022/01/150)Jiangsu Provincial Qinglan Project,the Special Fund for Urban and Rural Construction and Development in Jiangsu Province.
文摘More and more accounts or devices are shared by multiple users in video applications,which makes it difficult to provide recommendation service.Existing recommendation schemes overlook multiuser sharing scenarios,and they cannot make effective use of the mixed information generated by multi-user when exploring users’potential interests.To solve these problems,this paper proposes an adaptive program recommendation system for multi-user sharing environment.Specifically,we first design an offline periodic identification module by building multi-user features and periodically predicting target user in future sessions,which can separate the profile of target user from mixed log records.Subsequently,an online recommendation module with adaptive timevarying exploration strategy is constructed by jointly using personal information and multi-user social information provided by identification module.On one hand,to learn the dynamic changes in user-interest,a time-varying linear upper confidence bound(LinUCB)based on personal information is designed.On the other hand,to reduce the risk of exploration,a timeinvariant LinUCB based on separated multi-user social information from one account/device is proposed to compute the quality scores of programs for each user,which is integrated into the time-varying LinUCB by cross-weighting strategy.Finally,experimental results validate the efficiency of the proposed scheme.
基金supported by the National Natural Science Foundation of China (No. 61977003),entitled “Research on learning style for adaptive learning: modelling, identification and applications”
文摘Smart learning systems provide relevant learning resources as a personalized bespoke package for learners based on their pedagogical needs and individual preferences.This paper introduces a learning style model to represent features of online learners.It also presents an enhanced recommendation method named Adaptive Recommendation based on Online Learning Style(AROLS),which implements learning resource adaptation by mining learners’behavioral data.First,AROLS creates learner clusters according to their online learning styles.Second,it applies Collaborative Filtering(CF)and association rule mining to extract the preferences and behavioral patterns of each cluster.Finally,it generates a personalized recommendation set of variable size.A real-world dataset is employed for some experiments.Results show that our online learning style model is conducive to the learners’data mining,and AROLS evidently outperforms the traditional CF method.
