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» Learning to recommend from positive evidence
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GECCO
2005
Springer
158views Optimization» more  GECCO 2005»
13 years 10 months ago
Applying both positive and negative selection to supervised learning for anomaly detection
This paper presents a novel approach of applying both positive selection and negative selection to supervised learning for anomaly detection. It first learns the patterns of the n...
Xiaoshu Hang, Honghua Dai
CHI
2011
ACM
12 years 8 months ago
When designing usability questionnaires, does it hurt to be positive?
When designing questionnaires there is a tradition of including items with both positive and negative wording to minimize acquiescence and extreme response biases. Two disadvantag...
Jeff Sauro, James R. Lewis
ICDM
2008
IEEE
183views Data Mining» more  ICDM 2008»
13 years 11 months ago
Collaborative Filtering for Implicit Feedback Datasets
A common task of recommender systems is to improve customer experience through personalized recommendations based on prior implicit feedback. These systems passively track differe...
Yifan Hu, Yehuda Koren, Chris Volinsky
EACL
2003
ACL Anthology
13 years 6 months ago
Classifying the Hungarian Web
In this paper we present some lessons learned from building vizsla, the keyword search and topic classification system used on the largest Hungarian portal, [origo.hu]. Based on ...
András Kornai, Marc Krellenstein, Michael M...
AAAI
2012
11 years 7 months ago
Learning Games from Videos Guided by Descriptive Complexity
In recent years, several systems have been proposed that learn the rules of a simple card or board game solely from visual demonstration. These systems were constructed for speci...
Lukasz Kaiser