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KDD
2005
ACM
109views Data Mining» more  KDD 2005»
16 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
KDD
2004
ACM
170views Data Mining» more  KDD 2004»
16 years 7 months ago
Why collective inference improves relational classification
Procedures for collective inference make simultaneous statistical judgments about the same variables for a set of related data instances. For example, collective inference could b...
David Jensen, Jennifer Neville, Brian Gallagher
KDD
2001
ACM
216views Data Mining» more  KDD 2001»
16 years 7 months ago
The distributed boosting algorithm
In this paper, we propose a general framework for distributed boosting intended for efficient integrating specialized classifiers learned over very large and distributed homogeneo...
Aleksandar Lazarevic, Zoran Obradovic
SDM
2009
SIAM
291views Data Mining» more  SDM 2009»
16 years 4 months ago
Detection and Characterization of Anomalies in Multivariate Time Series.
Anomaly detection in multivariate time series is an important data mining task with applications to ecosystem modeling, network traffic monitoring, medical diagnosis, and other d...
Christopher Potter, Haibin Cheng, Pang-Ning Tan, S...
KDD
2009
ACM
298views Data Mining» more  KDD 2009»
16 years 1 months ago
Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering
One-Class Collaborative Filtering (OCCF) is a task that naturally emerges in recommender system settings. Typical characteristics include: Only positive examples can be observed, ...
Rong Pan, Martin Scholz