The Short-term User Modeling for Predictive Applications
暂无分享,去创建一个
[1] Victor Carneiro,et al. Distributed architecture for k-nearest neighbors recommender systems , 2014, World Wide Web.
[2] S. Verma,et al. Web usage pattern analysis through web logs: A review , 2012, 2012 Ninth International Conference on Computer Science and Software Engineering (JCSSE).
[3] Guangyuan Piao. Towards Comprehensive User Modeling on the Social Web for Personalized Link Recommendations , 2016, UMAP.
[4] Mária Bieliková,et al. Personalized Recommendation for Individual Users Based on the Group Recommendation Principles , 2013 .
[5] Alfredo Vellido Alcacena,et al. Predictive models in churn data mining: a review , 2007 .
[6] Eelco Herder. An Analysis of User Behavior on the Web - Understanding the Web and Its Users , 2007 .
[7] M. HamidR.Jamali,et al. Website usage metrics: A re-assessment of session data , 2008, Inf. Process. Manag..
[8] Mingjie Tan,et al. Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method , 2015, Int. J. Emerg. Technol. Learn..
[9] Jimeng Sun,et al. Temporal recommendation on graphs via long- and short-term preference fusion , 2010, KDD.
[10] Ryen W. White,et al. Leaving so soon?: understanding and predicting web search abandonment rationales , 2012, CIKM.
[11] Vanja Josifovski,et al. Web-scale user modeling for targeting , 2012, WWW.
[12] Monika Kukar-Kinney,et al. The determinants of consumers’ online shopping cart abandonment , 2010 .
[13] Abhinandan Das,et al. Google news personalization: scalable online collaborative filtering , 2007, WWW '07.
[14] Leonard M. Sander,et al. A Generalized Voter Model on Complex Networks , 2009 .
[15] Yuan Cheng,et al. Model bloggers' interests based on forgetting mechanism , 2008, WWW.
[16] Tommy W. S. Chow,et al. A novel feature selection method and its application , 2013, Journal of Intelligent Information Systems.
[17] Mária Bieliková,et al. Modeling the Reusable Content of Adaptive Web-Based Applications Using an Ontology , 2008, Advances in Semantic Media Adaptation and Personalization.
[18] Mária Bieliková,et al. Student behavior in a web-based educational system: Exit intent prediction , 2016, Eng. Appl. Artif. Intell..
[19] Andreas K. Maier,et al. Fast and robust selection of highly-correlated features in regression problems , 2017, 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA).
[20] Peter Werner,et al. Building and evaluating nonobvious user profiles for visitors of Web sites , 2004, Proceedings. IEEE International Conference on e-Commerce Technology, 2004. CEC 2004..
[21] Ryen W. White,et al. Modeling dwell time to predict click-level satisfaction , 2014, WSDM.
[22] Yang Song,et al. Context-aware web search abandonment prediction , 2014, SIGIR.
[23] Philip H. W. Leong,et al. Grammar-Based Feature Generation for Time-Series Prediction , 2015 .
[24] Fan Li,et al. Model Selection Strategy for Customer Attrition Risk Prediction in Retail Banking , 2011, AusDM.
[25] Mária Bieliková,et al. Context-Based Satisfaction Modelling for Personalized Recommendations , 2013, 2013 8th International Workshop on Semantic and Social Media Adaptation and Personalization.
[26] Jerzy Stefanowski,et al. Types of minority class examples and their influence on learning classifiers from imbalanced data , 2015, Journal of Intelligent Information Systems.
[27] Aleksandr Chuklin,et al. Potential good abandonment prediction , 2012, WWW.
[28] J. Evans. Straightforward Statistics for the Behavioral Sciences , 1995 .
[29] Yang Yang,et al. Dynamic User Attribute Discovery on Social Media , 2016, APWeb.
[30] Raymond J. Mooney,et al. Learning to Disambiguate Search Queries from Short Sessions , 2009, ECML/PKDD.
[31] Daniel Gayo-Avello,et al. A survey on session detection methods in query logs and a proposal for future evaluation , 2009, Inf. Sci..
[32] Róbert Móro,et al. ALEF: From Application to Platform for Adaptive Collaborative Learning , 2014, Recommender Systems for Technology Enhanced Learning.
[33] Priyanka Patel,et al. Improve Heuristics for User Session Identification through Web Server Log in Web Usage Mining , 2014 .
[34] Yi Li,et al. A hybrid recommendation algorithm adapted in e-learning environments , 2012, World Wide Web.
[35] Michael P. O'Mahony,et al. Evaluating the Relative Performance of Collaborative Filtering Recommender Systems , 2015, J. Univers. Comput. Sci..
[36] Bracha Shapira,et al. Recommender Systems Handbook , 2015, Springer US.
[37] Michael J. Pazzani,et al. Adaptive News Access , 2007, The Adaptive Web.
[38] Myra Spiliopoulou,et al. A Framework for the Evaluation of Session Reconstruction Heuristics in Web-Usage Analysis , 2003, INFORMS J. Comput..
[39] Cristina Conati. How to Evaluate Models of User Affect? , 2004, ADS.
[40] Elaine Rich,et al. Stereotypes and User Modeling , 1989 .
[41] Dursun Delen,et al. A comparative analysis of machine learning techniques for student retention management , 2010, Decis. Support Syst..
[42] อนิรุธ สืบสิงห์,et al. Data Mining Practical Machine Learning Tools and Techniques , 2014 .
[43] Dongyan Zhao,et al. Mining User Interests in Web Logs of an Online News Service Based on Memory Model , 2013, 2013 IEEE Eighth International Conference on Networking, Architecture and Storage.
[44] Bogumil Kaminski,et al. Social-Network Influence on Telecommunication Customer Attrition , 2011, KES-AMSTA.
[45] Bofeng Zhang,et al. User Model Evolution Algorithm: Forgetting and Reenergizing User Preference , 2011, 2011 International Conference on Internet of Things and 4th International Conference on Cyber, Physical and Social Computing.
[46] George Karypis,et al. A Comprehensive Survey of Neighborhood-based Recommendation Methods , 2011, Recommender Systems Handbook.
[47] Jianjun Yu,et al. Combining long-term and short-term user interest for personalized hashtag recommendation , 2015, Frontiers of Computer Science.
[48] Madian Khabsa,et al. Detecting Good Abandonment in Mobile Search , 2016, WWW.
[49] Sujata Joshi,et al. Customer Experience Management: An Exploratory Study on the Parameters Affecting Customer Experience for Cellular Mobile Services of a Telecom Company , 2014 .
[50] Arthur C. Graesser,et al. To Quit or Not to Quit: Predicting Future Behavioral Disengagement from Reading Patterns , 2014, Intelligent Tutoring Systems.
[51] Theodore Vasiloudis,et al. Predicting Session Length in Media Streaming , 2017, SIGIR.
[52] A. Sangodiah. Holistic Prediction of Student Attrition in Higher Learning Institutions in Malaysia Using Support Vector Machine Model , 2014 .
[53] Vincent S. Tseng,et al. Efficient mining and prediction of user behavior patterns in mobile web systems , 2006, Inf. Softw. Technol..