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Item-item collaborative filtering
Results: 24

#	Item
11	Logistic Matrix Factorization for Implicit Feedback Data Christopher C. Johnson Spotify New York, NY 10011 Add to Reading List Source URL: stanford.edu Language: English Computing Information science Information retrieval Recommender systems Collaboration Collective intelligence Collaborative filtering Latent semantic analysis Probabilistic latent semantic analysis Apache Spark Non-negative matrix factorization Item-item collaborative filtering
12	Exploiting Implicit Item Relationships for Recommender Systems Zhu Sun, Guibing Guo∗ , and Jie Zhang School of Computer Engineering, Nanyang Technological University, Singapore ∗ School of Information Systems, Singap Add to Reading List Source URL: www.ntu.edu.sg Language: English - Date: 2015-04-13 01:06:23 Collective intelligence Recommender systems Collaboration Collaborative filtering Cold start Association rule learning
13	Model-Based Collaborative Filtering Analysis of Student Response Data: Machine-Learning Item Response Theory ∗ † Add to Reading List Source URL: www.humanlearningmachine.com Language: English - Date: 2012-04-30 19:10:05
14	IRT in the Style of Collaborative Filtering Machine-Learning Item Response Theory Yoav Bergner, Stefan Dröschler, Gerd Kortemeyer, Saif Rayyan, Daniel Seaton, David Pritchard Add to Reading List Source URL: www.humanlearningmachine.com Language: English - Date: 2012-07-13 19:33:03
15	Industry Report Amazon.com Recommendations Item-to-Item Collaborative Filtering Greg Linden, Brent Smith, and Jeremy York • Amazon.com Add to Reading List Source URL: www.cs.umd.edu Language: English - Date: 2008-05-05 09:57:42
16	Eigentaste 5.0: Constant-Time Adaptability in a Recommender System Using Item Clustering Tavi Nathanson Ephrat Bitton Add to Reading List Source URL: goldberg.berkeley.edu Language: English - Date: 2007-08-31 03:30:28 Information systems Science Collaboration Social information processing Information retrieval Collaborative filtering Recommender system Cold start Cluster analysis Statistics Information science Collective intelligence
17	Improving Social Recommendations by applying a Personalized Item Clustering policy Georgios Alexandridis Georgios Siolas Add to Reading List Source URL: ceur-ws.org Language: English - Date: 2013-10-21 14:24:22 Machine learning Data mining Geostatistics Collaborative filtering Recommender system Hierarchical clustering Correlation clustering Spectral clustering K-means clustering Statistics Cluster analysis Multivariate statistics
18	Feature-based factorized Bilinear Similarity Model for Cold-Start Top-n Item Recommendation∗ Mohit Sharma †, Jiayu Zhou ‡, Junling Hu‡ , George Karypis† Abstract Recommending new items to existing users has rem Add to Reading List Source URL: glaros.dtc.umn.edu Language: English - Date: 2015-04-14 08:16:45 Science Information retrieval Psychometrics Multivariate statistics Educational psychology Collaborative filtering Recommender system Factor analysis Low-rank approximation Statistics Information science Collective intelligence
19	This is the author’s version of a work that was submitted/accepted for publication in the following source: Abdel-Hafez, Ahmad, Xu, Yue, & Tian, Nan[removed]Item reputation-aware recommender systems. In IIWAS ’14 : Pr Add to Reading List Source URL: eprints.qut.edu.au Language: English - Date: 2015-03-19 19:32:48 Social information processing Recommender systems Human–computer interaction Collaboration Collaborative filtering GroupLens Research Cold start Reputation system Personalization Information science Information retrieval Collective intelligence
20	Eigentaste 5.0: Constant-Time Adaptability in a Recommender System Using Item Clustering Tavi Nathanson Ephrat Bitton Add to Reading List Source URL: www.ieor.berkeley.edu Language: English - Date: 2007-08-31 03:30:28 Information systems Science Collaboration Social information processing Information retrieval Collaborative filtering Recommender system Cold start Cluster analysis Statistics Information science Collective intelligence