基于情景感知的用户兴趣推荐模型
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摘要
随着电子商务和互联网的发展与普及,面向用户的个性化推荐越来越被重视,传统的用户兴趣模型只考虑到用户本身对项目的行为,忽略了用户当时所处情景。因此文中提出了基于情景感知的用户兴趣模型,将用户的浏览行为与情景因素相结合,从两个方面深度挖掘了用户对项目的兴趣,明确了用户对项目的关注度,从而准确地为用户进行聚类,并根据用户聚类的结果对目标用户进行推荐。实验结果表明,该推荐模型的准确率高于其他传统推荐算法的准确率,本模型能更好地挖掘用户兴趣,适应用户的兴趣变化,并且能够更好地解决用户面临的众多信息无从挑选的问题,提高了用户的满意度。因此,需要从多个角度挖掘用户隐藏的信息,能够更好地为用户提供个性化的推荐。
With the development and popularization of electronic commerce and Internet,the user-oriented personalized recommendation is getting more and more attention,the traditional user interest models only consider the user's own behavior towards the project,while ignoring the user's scene at that time.Aiming at this problem,this paper proposed a user interest model based on scene perception,combined the user's browsing behavior with the situational factors,deeply mined user interest in the project from two aspects,cleared user awareness of the project,thus accurate clustered for users,and target users based on user clustering results were recommended.Experimental results show that the accuracy of this model recommendation is higher than that of other traditional recommendation algorithms.It can better mine users' interests,adapt to the changes of users' interests,better solve the problem that users have no way to choose a lot of information,and improve users' satisfaction.Therefore,it is necessary to excavate the hidden information of users from multiple perspectives so as to better provide personalized recommendations for users.
With the development and popularization of electronic commerce and Internet,the user-oriented personalized recommendation is getting more and more attention,the traditional user interest models only consider the user's own behavior towards the project,while ignoring the user's scene at that time.Aiming at this problem,this paper proposed a user interest model based on scene perception,combined the user's browsing behavior with the situational factors,deeply mined user interest in the project from two aspects,cleared user awareness of the project,thus accurate clustered for users,and target users based on user clustering results were recommended.Experimental results show that the accuracy of this model recommendation is higher than that of other traditional recommendation algorithms.It can better mine users' interests,adapt to the changes of users' interests,better solve the problem that users have no way to choose a lot of information,and improve users' satisfaction.Therefore,it is necessary to excavate the hidden information of users from multiple perspectives so as to better provide personalized recommendations for users.
引文
[1] 陈续阳.基于情境感知的用户个性化推荐方法研究[D].成都:成都理工大学,2016.
[2] 周朴雄,薛玮炜,赵龙文.基于个性化情境的Multi-Agent信息推荐研究[J].情报杂志,2015,34(5):180-184.
[3] 曾子明,陈贝贝.移动环境下基于情境感知的个性化阅读推荐研究[J].情报理论与实践,2015,38(12):31-36.
[4] DEY A K .Providing architectural support for building contex- taware applications[D].Atlanta Batanical:Georgia Institute of Technology,2000.
[5] 刘红霞.基于协同过滤技术的推荐系统综述[J].信息安全与技术,2016,7(3):24-26.
[6] ADOMAVICIUS G,SANKARANARAYANAN R,SEN S,et al.Incorporating contextual information in recommender systems using a multidimensional approach[J].ACM Transactions on Information Systems,2005,23(1):103-145.
[7] EBESU T,FANG Y .Neural Citation Network for Context-Aware Citation Recommendation[C]//40th International ACM SIGIR Conference.ACM,2017.
[8] MAO M,LU J,ZHANG G,et al.Multirelational Social Recommendations via Multigraph Ranking[J].IEEE Transactions on Cybernetics,2017,47(12):4049-4061.
[9] BRAUNHOFER M,RICCI F,BéATRICE L,et al.A Context-Aware Model for Proactive Recommender Systems in the Tou-rism Domain[C]//International Conference on Human-compu-ter Interaction with Mobile Devices & Services Adjunct.ACM,2015.
[10] OLIVEIRA T,THOMAS M A,ESPADANAL M .Assessing the determinants of cloud computing adoption:An analysis of the manufacturing and services sectors[J].Information & Management,2014,51(5):497-510.
[11] ABDRABBAH S B,AYACHI R,AMOR N B .Social Activities Recommendation System for Students in Smart Campus[J].Smart Innaration,Systems and Technologies,2017,76(5):461-470.
[12] YAO C B .Constructing a User-Friendly and Smart Ubiquitous Personalized Learning Environment by Using a Context-Aware Mechanism[J].IEEE Transactions on Learning Technologies,2017,10(1):104-114.
[13] ZHU H,XIONG H,GE Y,et al.Mobile app recommendations with security and privacy awareness[C]//Proceedings of the 20th International Conference on Knowledge Discovery and Data Mining.2014:951-960.
[14] TANG L,LIU E Y .Joint User-Entity Representation Learning for Event Recommendation in Social Network[C]//2017 IEEE 33rd International Conference on Data Engineering (ICDE).IEEE,2017.
[15] XIA P,ZHAI S,LIU B,et al.Design of reciprocal recommendation systems for online dating[J].Social Network Analysis & Mining,2016,6(1):32-33.
[16] TATAR A,AMORIM M D D,FDIDA S,et al.A survey on predicting the popularity of web content[J].Journal of Internet Services and Applications,2014,5(1):8-9.
[17] GUILLE A,FAVRE C.Event Detection,Tracking and Visualization in Twitter:A Mention-anomaly-based Approach[J].Social Network Analysis & Mining,2015,5(1):18-19.
[18] XU J,ZHONG Y,ZHU W,et al.Trust-based context-aware mobile social network service recommendation[J].Wuhan University Journal of Natural Sciences,2017,22(2):149-156.
[19] GARG S,GENTRY C,HALEVI S,et al.Functional encryption without obfuscation[M]//Theory of Cryptography.Springer Berlin Heidelberg,2016.
[20] 任星怡,宋美娜,宋俊德.基于用户签到行为的兴趣点推荐[J].计算机学报,2017,40(1):28-51.
[21] LIAN D,ZHENG K,GE Y,et al.GeoMF++:Scalable Location Recommendation via Joint Geographical Modeling and Matrix Factorization[J].ACM Transactions on Information Systems,2018,36(3):1-29.
[22] LIN H T.A traveling memory based upon location-based services and social network sites[J].Journal of the Chinese Institute of Engineers,2015,38(2):181-190.
[23] MEMON I,LING C,MAJID A,et al.Travel Recommendation Using Geo-tagged Photos in Social Media for Tourist [J].Wireless Personal Communications,2015,80(4):1347-1362.
[24] SUN Y,FAN H C,BAKILLAH M,et al.Road-based travel recommendation using geo-tagged images [J].Computers,Environment and Urban Systems,2015,53(9):110-122.
[2] 周朴雄,薛玮炜,赵龙文.基于个性化情境的Multi-Agent信息推荐研究[J].情报杂志,2015,34(5):180-184.
[3] 曾子明,陈贝贝.移动环境下基于情境感知的个性化阅读推荐研究[J].情报理论与实践,2015,38(12):31-36.
[4] DEY A K .Providing architectural support for building contex- taware applications[D].Atlanta Batanical:Georgia Institute of Technology,2000.
[5] 刘红霞.基于协同过滤技术的推荐系统综述[J].信息安全与技术,2016,7(3):24-26.
[6] ADOMAVICIUS G,SANKARANARAYANAN R,SEN S,et al.Incorporating contextual information in recommender systems using a multidimensional approach[J].ACM Transactions on Information Systems,2005,23(1):103-145.
[7] EBESU T,FANG Y .Neural Citation Network for Context-Aware Citation Recommendation[C]//40th International ACM SIGIR Conference.ACM,2017.
[8] MAO M,LU J,ZHANG G,et al.Multirelational Social Recommendations via Multigraph Ranking[J].IEEE Transactions on Cybernetics,2017,47(12):4049-4061.
[9] BRAUNHOFER M,RICCI F,BéATRICE L,et al.A Context-Aware Model for Proactive Recommender Systems in the Tou-rism Domain[C]//International Conference on Human-compu-ter Interaction with Mobile Devices & Services Adjunct.ACM,2015.
[10] OLIVEIRA T,THOMAS M A,ESPADANAL M .Assessing the determinants of cloud computing adoption:An analysis of the manufacturing and services sectors[J].Information & Management,2014,51(5):497-510.
[11] ABDRABBAH S B,AYACHI R,AMOR N B .Social Activities Recommendation System for Students in Smart Campus[J].Smart Innaration,Systems and Technologies,2017,76(5):461-470.
[12] YAO C B .Constructing a User-Friendly and Smart Ubiquitous Personalized Learning Environment by Using a Context-Aware Mechanism[J].IEEE Transactions on Learning Technologies,2017,10(1):104-114.
[13] ZHU H,XIONG H,GE Y,et al.Mobile app recommendations with security and privacy awareness[C]//Proceedings of the 20th International Conference on Knowledge Discovery and Data Mining.2014:951-960.
[14] TANG L,LIU E Y .Joint User-Entity Representation Learning for Event Recommendation in Social Network[C]//2017 IEEE 33rd International Conference on Data Engineering (ICDE).IEEE,2017.
[15] XIA P,ZHAI S,LIU B,et al.Design of reciprocal recommendation systems for online dating[J].Social Network Analysis & Mining,2016,6(1):32-33.
[16] TATAR A,AMORIM M D D,FDIDA S,et al.A survey on predicting the popularity of web content[J].Journal of Internet Services and Applications,2014,5(1):8-9.
[17] GUILLE A,FAVRE C.Event Detection,Tracking and Visualization in Twitter:A Mention-anomaly-based Approach[J].Social Network Analysis & Mining,2015,5(1):18-19.
[18] XU J,ZHONG Y,ZHU W,et al.Trust-based context-aware mobile social network service recommendation[J].Wuhan University Journal of Natural Sciences,2017,22(2):149-156.
[19] GARG S,GENTRY C,HALEVI S,et al.Functional encryption without obfuscation[M]//Theory of Cryptography.Springer Berlin Heidelberg,2016.
[20] 任星怡,宋美娜,宋俊德.基于用户签到行为的兴趣点推荐[J].计算机学报,2017,40(1):28-51.
[21] LIAN D,ZHENG K,GE Y,et al.GeoMF++:Scalable Location Recommendation via Joint Geographical Modeling and Matrix Factorization[J].ACM Transactions on Information Systems,2018,36(3):1-29.
[22] LIN H T.A traveling memory based upon location-based services and social network sites[J].Journal of the Chinese Institute of Engineers,2015,38(2):181-190.
[23] MEMON I,LING C,MAJID A,et al.Travel Recommendation Using Geo-tagged Photos in Social Media for Tourist [J].Wireless Personal Communications,2015,80(4):1347-1362.
[24] SUN Y,FAN H C,BAKILLAH M,et al.Road-based travel recommendation using geo-tagged images [J].Computers,Environment and Urban Systems,2015,53(9):110-122.