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Intelligent Techniques for Web Personalization

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Cover of 'Intelligent Techniques for Web Personalization'

Table of Contents

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    Book Overview
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    Chapter 1 Intelligent Techniques for Web Personalization
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    Chapter 2 Modeling Web Navigation: Methods and Challenges
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    Chapter 3 The Traits of the Personable
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    Chapter 4 Addressing Users’ Privacy Concerns for Improving Personalization Quality: Towards an Integration of User Studies and Algorithm Evaluation
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    Chapter 5 Case-Based Recommender Systems: A Unifying View
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    Chapter 6 Improving the Performance of Recommender Systems That Use Critiquing
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    Chapter 7 Hybrid Systems for Personalized Recommendations
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    Chapter 8 Collaborative Filtering Using Associative Neural Memory
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    Chapter 9 Scaling Down Candidate Sets Based on the Temporal Feature of Items for Improved Hybrid Recommendations
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    Chapter 10 Discovering Interesting Navigations on a Web Site Using SAM I
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    Chapter 11 Personalisation of Web Search
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    Chapter 12 The Compass Filter: Search Engine Result Personalization Using Web Communities
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    Chapter 13 Predicting Web Information Content
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    Chapter 14 Mobile Portal Personalization: Tools and Techniques
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    Chapter 15 IKUM: An Integrated Web Personalization Platform Based on Content Structures and User Behavior
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    Chapter 16 A Semantic-Based User Privacy Protection Framework for Web Services
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    Chapter 17 Web Personalisation for Users Protection: A Multi-agent Method
Attention for Chapter 6: Improving the Performance of Recommender Systems That Use Critiquing
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Chapter title
Improving the Performance of Recommender Systems That Use Critiquing
Chapter number 6
Book title
Intelligent Techniques for Web Personalization
Published by
Springer, Berlin, Heidelberg, January 2005
DOI 10.1007/11577935_6
Book ISBNs
978-3-54-029846-5, 978-3-54-031655-8
Authors

Lorraine McGinty, Barry Smyth, McGinty, Lorraine, Smyth, Barry

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Ireland 1 10%
Unknown 9 90%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 3 30%
Professor 2 20%
Student > Master 2 20%
Researcher 1 10%
Student > Ph. D. Student 1 10%
Other 0 0%
Unknown 1 10%
Readers by discipline Count As %
Computer Science 8 80%
Business, Management and Accounting 1 10%
Unknown 1 10%