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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
  2. Altmetric Badge
    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 15: IKUM: An Integrated Web Personalization Platform Based on Content Structures and User Behavior
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Chapter title
IKUM: An Integrated Web Personalization Platform Based on Content Structures and User Behavior
Chapter number 15
Book title
Intelligent Techniques for Web Personalization
Published by
Springer, Berlin, Heidelberg, January 2005
DOI 10.1007/11577935_15
Book ISBNs
978-3-54-029846-5, 978-3-54-031655-8
Authors

Magdalini Eirinaki, Joannis Vlachakis, Sarabjot Singh Anand, Eirinaki, Magdalini, Vlachakis, Joannis, Anand, Sarabjot, Anand, Sarabjot Singh

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 13%
Venezuela, Bolivarian Republic of 1 13%
Unknown 6 75%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 38%
Unspecified 1 13%
Professor 1 13%
Researcher 1 13%
Professor > Associate Professor 1 13%
Other 1 13%
Readers by discipline Count As %
Computer Science 5 63%
Unspecified 1 13%
Agricultural and Biological Sciences 1 13%
Physics and Astronomy 1 13%