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KDD
2007
ACM
191views Data Mining» more  KDD 2007»
14 years 7 months ago
Modeling relationships at multiple scales to improve accuracy of large recommender systems
The collaborative filtering approach to recommender systems predicts user preferences for products or services by learning past useritem relationships. In this work, we propose no...
Robert M. Bell, Yehuda Koren, Chris Volinsky
KDD
2005
ACM
109views Data Mining» more  KDD 2005»
14 years 7 months ago
Overcoming Incomplete User Models in Recommendation Systems Via an Ontology
Abstract. To make accurate recommendations, recommendation systems currently require more data about a customer than is usually available. We conjecture that the weaknesses are due...
Vincent Schickel-Zuber, Boi Faltings
AAAI
2012
11 years 9 months ago
A Sequential Decision Approach to Ordinal Preferences in Recommender Systems
We propose a novel sequential decision approach to modeling ordinal ratings in collaborative filtering problems. The rating process is assumed to start from the lowest level, eva...
Truyen Tran, Dinh Q. Phung, Svetha Venkatesh
KDD
2010
ACM
265views Data Mining» more  KDD 2010»
13 years 11 months ago
Combining predictions for accurate recommender systems
We analyze the application of ensemble learning to recommender systems on the Netflix Prize dataset. For our analysis we use a set of diverse state-of-the-art collaborative filt...
Michael Jahrer, Andreas Töscher, Robert Legen...
LWA
2007
13 years 8 months ago
Know the Right People? Recommender Systems for Web 2.0
Web 2.0 applications like Flickr, YouTube, or Del.icio.us are increasingly popular online communities for creating, editing and sharing content. However, the rapid increase in siz...
Stefan Siersdorfer, Sergej Sizov, Paul Clough