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User Modeling, Adaptation, and Personalization

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Cover of 'User Modeling, Adaptation, and Personalization'

Table of Contents

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    Book Overview
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    Chapter 1 Personalized Network Updates: Increasing Social Interactions and Contributions in Social Networks
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    Chapter 2 Realistic Simulation of Museum Visitors’ Movements as a Tool for Assessing Sensor-Based User Models
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    Chapter 3 GECKOmmender: Personalised Theme and Tour Recommendations for Museums
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    Chapter 4 Property-Based Interest Propagation in Ontology-Based User Model
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    Chapter 5 EEG Estimates of Engagement and Cognitive Workload Predict Math Problem Solving Outcomes
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    Chapter 6 Preference Relation Based Matrix Factorization for Recommender Systems
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    Chapter 7 A Framework for Modeling Trustworthiness of Users in Mobile Vehicular Ad-Hoc Networks and Its Validation through Simulated Traffic Flow
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    Chapter 8 A Comparative Study of Users’ Microblogging Behavior on Sina Weibo and Twitter
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    Chapter 9 Modeling Multiple Distributions of Student Performances to Improve Predictive Accuracy
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    Chapter 10 A Simple But Effective Method to Incorporate Trusted Neighbors in Recommender Systems
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    Chapter 11 Exploring Gaze Data for Determining User Learning with an Interactive Simulation
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    Chapter 12 Studies to Determine User Requirements Regarding In-Home Monitoring Systems
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    Chapter 13 Improving Tensor Based Recommenders with Clustering
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    Chapter 14 Models of User Engagement
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    Chapter 15 Improving the Performance of Unit Critiquing
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    Chapter 16 Enhanced Semantic TV-Show Representation for Personalized Electronic Program Guides
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    Chapter 17 Attention and Selection in Online Choice Tasks
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    Chapter 18 Investigating Explanations to Justify Choice
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    Chapter 19 The Effect of Suspicious Profiles on People Recommenders
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    Chapter 20 Users and Noise: The Magic Barrier of Recommender Systems
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    Chapter 21 Improving Construct Validity Yields Better Models of Systematic Inquiry, Even with Less Information
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    Chapter 22 Inferring Personality of Online Gamers by Fusing Multiple-View Predictions
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    Chapter 23 Towards Adaptive Information Visualization: On the Influence of User Characteristics
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    Chapter 24 WTF? detecting students who are conducting inquiry without thinking fastidiously
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    Chapter 25 User Modeling, Adaptation, and Personalization
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    Chapter 26 A Multi-faceted User Model for Twitter
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    Chapter 27 Evaluating Rating Scales Personality
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    Chapter 28 Automating the Modeling of Learners’ Erroneous Behaviors in Model-Tracing Tutors
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    Chapter 29 Using Touch as a Predictor of Effort: What the iPad Can Tell Us about User Affective State
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    Chapter 30 User Modeling, Adaptation, and Personalization
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    Chapter 31 User Modeling, Adaptation, and Personalization
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    Chapter 32 Adaptive Score Reports
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    Chapter 33 Improving Matrix Factorization Techniques of Student Test Data with Partial Order Constraints
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    Chapter 34 Evaluating an Implementation of an Adaptive Game-Based Learning Architecture
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    Chapter 35 Towards a Generic Model for User Assistance
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    Chapter 36 Resolving Data Sparsity and Cold Start in Recommender Systems
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    Chapter 37 Data Mining for Adding Adaptive Interventions to Exploratory and Open-Ended Environments
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    Chapter 38 Formalising Human Mental Workload as Non-monotonic Concept for Adaptive and Personalised Web-Design
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    Chapter 39 Detecting, Acquiring and Exploiting Contextual Information in Personalized Services
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    Chapter 40 Multi-source Provenance-aware User Interest Profiling on the Social Semantic Web
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    Chapter 41 User Feedback and Preferences Mining
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    Chapter 42 Ubiquitous Fuzzy User Modeling for Multi-application Environments by Mining Socially Enhanced Online Traces
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    Chapter 43 Facilitating Code Example Search on the Web through Expertise Personalization
Overall attention for this book and its chapters
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Mentioned by

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15 X users
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2 Wikipedia pages
q&a
1 Q&A thread

Citations

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10 Dimensions

Readers on

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378 Mendeley
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Title
User Modeling, Adaptation, and Personalization
Published by
User Modeling Adaptation and Personalization, January 2012
DOI 10.1007/978-3-642-31454-4
ISBNs
978-3-64-231453-7, 978-3-64-231454-4
Editors

Judith Masthoff, Bamshad Mobasher, Michel C. Desmarais, Roger Nkambou

X Demographics

X Demographics

The data shown below were collected from the profiles of 15 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 378 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 378 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 <1%
Student > Doctoral Student 2 <1%
Lecturer 1 <1%
Student > Master 1 <1%
Student > Postgraduate 1 <1%
Other 0 0%
Unknown 371 98%
Readers by discipline Count As %
Computer Science 4 1%
Business, Management and Accounting 1 <1%
Psychology 1 <1%
Engineering 1 <1%
Unknown 371 98%