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CIKM
2001
Springer
14 years 3 months ago
Selecting Relevant Instances for Efficient and Accurate Collaborative Filtering
Collaborative filtering uses a database about consumers’ preferences to make personal product recommendations and is achieving widespread success in both E-Commerce and Informat...
Kai Yu, Xiaowei Xu, Martin Ester, Hans-Peter Krieg...
ICPR
2008
IEEE
15 years 4 days ago
Efficient user preference predictions using collaborative filtering
Two major challenges in collaborative filtering are the efficiency of the algorithms and the quality of the recommendations. A variety of machine learning methods have been applie...
C. Lee Giles, Yang Song
KDD
2009
ACM
223views Data Mining» more  KDD 2009»
14 years 11 months ago
Collaborative filtering with temporal dynamics
Customer preferences for products are drifting over time. Product perception and popularity are constantly changing as new selection emerges. Similarly, customer inclinations are ...
Yehuda Koren
AH
2006
Springer
14 years 2 months ago
eDAADe: An Adaptive Recommendation System for Comparison and Analysis of Architectural Precedents
We built a Web-based adaptive recommendation system for students to select and suggest architectural cases when they analyze "Case Study" work within the architectural de...
Shu-Feng Pan, Ji-Hyun Lee
KDD
2009
ACM
227views Data Mining» more  KDD 2009»
14 years 11 months ago
Efficiently learning the accuracy of labeling sources for selective sampling
Many scalable data mining tasks rely on active learning to provide the most useful accurately labeled instances. However, what if there are multiple labeling sources (`oracles...
Pinar Donmez, Jaime G. Carbonell, Jeff Schneider