Case Study 4: Collaborative Filtering Probabilistic Models for Matrix Factorization Cold-Start Problem Machine Learning/Statistics for Big Data

Source URL: courses.cs.washington.edu

Digital Library Curriculum Development Module 7-c: Recommender Systems (Draft, )

Source URL: curric.dlib.vt.edu

• Information science
• Recommender systems
• Information systems
• Computing
• Collective intelligence
• Information retrieval
• Humancomputer interaction
• Collaborative filtering
• Cold start
• Preference elicitation
• Information filtering system
• Personalization

Journal of Machine Learning Research3622 Submitted 10/11; PublishedSVDFeature: A Toolkit for Feature-based Collaborative Filtering Tianqi Chen

Source URL: www0.cs.ucl.ac.uk

Journal of Machine Learning Research3622 Submitted 10/11; PublishedSVDFeature: A Toolkit for Feature-based Collaborative Filtering Tianqi Chen

Source URL: www0.cs.ucl.ac.uk

BPR: Bayesian Personalized Ranking from Implicit Feedback Steffen Rendle, Christoph Freudenthaler, Zeno Gantner and Lars Schmidt-Thieme {srendle, freudenthaler, gantner, schmidt-thieme}@ismll.de Machine Learning Lab, Un

Source URL: www.algo.uni-konstanz.de

• Algebra
• Mathematics
• Information science
• Linear algebra
• Matrix theory
• Recommender systems
• Statistical theory
• Collaborative filtering
• Machine learning
• Loss function
• Singular value decomposition
• Artificial neural network

Implicit Look-Alike Modelling in Display Ads Transfer Collaborative Filtering to CTR Estimation Weinan Zhang1,2(B) , Lingxi Chen1 , and Jun Wang1,2 1 University College London, London, UK

Source URL: www0.cs.ucl.ac.uk

Brian McFee Page 1 of 5 Brian McFee Center for Data Science

Source URL: bmcfee.github.io

• Information science
• Information retrieval
• Music information retrieval
• Music technology
• Music software
• International Society for Music Information Retrieval
• Multimedia
• International Conference on Machine Learning
• Retrieval
• McFee
• Recommender system
• Collaborative filtering

Collaborative Filtering for Implicit Feedback Datasets Yifan Hu AT&T Labs – Research Florham Park, NJYehuda Koren∗

Source URL: yifanhu.net

• Information science
• Information retrieval
• Collective intelligence
• Collaboration
• Information
• Recommender systems
• Collaborative filtering
• Personalization
• Cold start

Benchmarking News Recommendations in a Living Lab Frank Hopfgartner1 , Benjamin Kille1 , Andreas Lommatzsch1 , Till Plumbaum1 , Torben Brodt2 , and Tobias Heintz2 1

Source URL: eprints.gla.ac.uk

• Recommender systems
• Collective intelligence
• Information systems
• Humancomputer interaction
• Collaborative filtering
• Cold start
• Personalization
• Text Retrieval Conference
• Session
• GroupLens Research
• Item-item collaborative filtering

Recommendation Based on Deduced Social Networks in an Educational Digital Library Monika Akbar Clifford A. Shaffer

Source URL: www.cs.utep.edu

• Collective intelligence
• Information science
• Collaboration
• Recommender systems
• Regression analysis
• Information retrieval
• Logistic regression
• Collaborative filtering
• Social navigation
• Usability
• Data source name