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Preference Learning

Overview of attention for book
Cover of 'Preference Learning'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Preference Learning: An Introduction
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    Chapter 2 A Preference Optimization Based Unifying Framework for Supervised Learning Problems
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    Chapter 3 Label Ranking Algorithms: A Survey
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    Chapter 4 Preference Learning and Ranking by Pairwise Comparison
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    Chapter 5 Decision Tree Modeling for Ranking Data
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    Chapter 6 Co-Regularized Least-Squares for Label Ranking
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    Chapter 7 A Survey on ROC-based Ordinal Regression
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    Chapter 8 Ranking Cases with Classification Rules
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    Chapter 9 A Survey and Empirical Comparison of Object Ranking Methods
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    Chapter 10 Dimension Reduction for Object Ranking
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    Chapter 11 Learning of Rule Ensembles for Multiple Attribute Ranking Problems
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    Chapter 12 Learning Lexicographic Preference Models
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    Chapter 13 Learning Ordinal Preferences on Multiattribute Domains: The Case of CP-nets
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    Chapter 14 Choice-Based Conjoint Analysis: Classification vs. Discrete Choice Models
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    Chapter 15 Learning Aggregation Operators for Preference Modeling
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    Chapter 16 Evaluating Search Engine Relevance with Click-Based Metrics
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    Chapter 17 Learning SVM Ranking Functions from User Feedback Using Document Metadata and Active Learning in the Biomedical Domain
  19. Altmetric Badge
    Chapter 18 Learning Preference Models in Recommender Systems
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    Chapter 19 Collaborative Preference Learning
  21. Altmetric Badge
    Chapter 20 Discerning Relevant Model Features in a Content-based Collaborative Recommender System
Attention for Chapter 5: Decision Tree Modeling for Ranking Data
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Mentioned by

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1 patent

Citations

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126 Dimensions

Readers on

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12 Mendeley
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Chapter title
Decision Tree Modeling for Ranking Data
Chapter number 5
Book title
Preference Learning
Published in
ADS, January 2010
DOI 10.1007/978-3-642-14125-6_5
Book ISBNs
978-3-64-214124-9, 978-3-64-214125-6
Authors

Philip L. H. Yu, Wai Ming Wan, Paul H. Lee, Yu, Philip L. H., Wan, Wai Ming, Lee, Paul H.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 25%
Professor 1 8%
Unspecified 1 8%
Researcher 1 8%
Professor > Associate Professor 1 8%
Other 0 0%
Unknown 5 42%
Readers by discipline Count As %
Computer Science 5 42%
Unspecified 1 8%
Agricultural and Biological Sciences 1 8%
Unknown 5 42%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 November 2020.
All research outputs
#7,541,325
of 23,007,053 outputs
Outputs from ADS
#9,305
of 37,438 outputs
Outputs of similar age
#48,812
of 164,896 outputs
Outputs of similar age from ADS
#272
of 795 outputs
Altmetric has tracked 23,007,053 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 37,438 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 29th percentile – i.e., 29% of its peers scored the same or lower than it.
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We're also able to compare this research output to 795 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.