Next-Term Student Performance Prediction: A Recommender Systems Approach∗ Mack Sweeney Computer Science George Mason University

Source URL: www.educationaldatamining.org

Schema-summarization in Linked-Data-based feature selection for recommender systems Azzurra Ragone1 , Paolo Tomeo2 , Corrado Magarelli2 , Tommaso Di Noia2 , Matteo Palmonari1 Andrea Maurino1 , Eugenio Di Sciascio2 1 1

Source URL: sisinflab.poliba.it

Personalisation and Recommender Systems in Digital Libraries Joint NSF-EU DELOS Working Group Report May 2003 Jamie Callan, Carnegie Mellon University, USA Alan Smeaton, Dublin City University, Ireland

Source URL: www.ercim.eu

• Information science
• Humancomputer interaction
• Library science
• Knowledge representation
• Personalization
• World Wide Web
• Digital library
• Library
• Recommender system
• User modeling
• Adaptation
• Draft:Tourism 4.0

Returning is Believing: Optimizing Long-term User Engagement in Recommender Systems Qingyun Wu, Hongning Wang Liangjie Hong∗

Source URL: www.hongliangjie.com

Journal of Machine Learning Research656 Submitted 3/08; Revised 11/08; Published 3/09 Scalable Collaborative Filtering Approaches for Large Recommender Systems

Source URL: www.jmlr.org

Automatic Representation for Life-Time Value Recommender Systems Automatic Representation for Life-Time Value Recommender Systems Assaf Hallak

Source URL: ewrl.files.wordpress.com

WORKSHOP REPORT Report on the SIGIR 2013 Workshop on Benchmarking Adaptive Retrieval and Recommender Systems Pablo Castells

Source URL: sigir.org

Combining Predictions for Accurate Recommender Systems Michael Jahrer Andreas Töscher

Source URL: elf-project.sourceforge.net

Content-based Recommender Systems John Bruntse Larsen Kongens Lyngby 2013

Source URL: www2.imm.dtu.dk

ON THE USE OF SEMANTIC FEEDBACK IN RECOMMENDER SYSTEMS Matthew Garden School of Computer Science

Source URL: www.cim.mcgill.ca