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User Modeling, Adaptation, and Personalization

Overview of attention for book
Cover of 'User Modeling, Adaptation, and Personalization'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Modeling Emotion and Its Expression in Virtual Humans
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    Chapter 2 AdHeat — An Influence-Based Diffusion Model for Propagating Hints to Personalize Social Ads
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    Chapter 3 Can Concept-Based User Modeling Improve Adaptive Visualization?
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    Chapter 4 Interweaving Public User Profiles on the Web
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    Chapter 5 Modeling Long-Term Search Engine Usage
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    Chapter 6 Analysis of Strategies for Building Group Profiles
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    Chapter 7 Contextual Slip and Prediction of Student Performance after Use of an Intelligent Tutor
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    Chapter 8 Working Memory Span and E-Learning: The Effect of Personalization Techniques on Learners’ Performance
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    Chapter 9 Scaffolding Self-directed Learning with Personalized Learning Goal Recommendations
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    Chapter 10 Instructional Video Content Employing User Behavior Analysis: Time Dependent Annotation with Levels of Detail
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    Chapter 11 A User-and Item-Aware Weighting Scheme for Combining Predictive User Models
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    Chapter 12 User Modeling, Adaptation, and Personalization
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    Chapter 13 Twitter, Sensors and UI: Robust Context Modeling for Interruption Management
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    Chapter 14 Ranking Feature Sets for Emotion Models Used in Classroom Based Intelligent Tutoring Systems
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    Chapter 15 Inducing Effective Pedagogical Strategies Using Learning Context Features
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    Chapter 16 “Yes!”: Using Tutor and Sensor Data to Predict Moments of Delight during Instructional Activities
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    Chapter 17 A Personalized Graph-Based Document Ranking Model Using a Semantic User Profile
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    Chapter 18 Interaction and Personalization of Criteria in Recommender Systems
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    Chapter 19 Collaborative Inference of Sentiments from Texts
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    Chapter 20 User Modeling, Adaptation, and Personalization
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    Chapter 21 IntrospectiveViews: An Interface for Scrutinizing Semantic User Models
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    Chapter 22 Analyzing Community Knowledge Sharing Behavior
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    Chapter 23 A Data-Driven Technique for Misconception Elicitation
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    Chapter 24 Modeling Individualization in a Bayesian Networks Implementation of Knowledge Tracing
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    Chapter 25 Detecting Gaming the System in Constraint-Based Tutors
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    Chapter 26 Bayesian Credibility Modeling for Personalized Recommendation in Participatory Media
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    Chapter 27 A Study on User Perception of Personality-Based Recommender Systems
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    Chapter 28 Compass to Locate the User Model I Need: Building the Bridge between Researchers and Practitioners in User Modeling
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    Chapter 29 myCOMAND Automotive User Interface: Personalized Interaction with Multimedia Content Based on Fuzzy Preference Modeling
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    Chapter 30 User Modeling for Telecommunication Applications: Experiences and Practical Implications
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    Chapter 31 Mobile Web Profiling: A Study of Off-Portal Surfing Habits of Mobile Users
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    Chapter 32 Personalized Implicit Learning in a Music Recommender System
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    Chapter 33 Personalised Pathway Prediction
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    Chapter 34 Towards a Customization of Rating Scales in Adaptive Systems
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    Chapter 35 Eye-Tracking Study of User Behavior in Recommender Interfaces
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    Chapter 36 Recommending Food: Reasoning on Recipes and Ingredients
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    Chapter 37 Disambiguating Search by Leveraging a Social Context Based on the Stream of User’s Activity
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    Chapter 38 Features of an Independent Open Learner Model Influencing Uptake by University Students
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    Chapter 39 Recognizing and Predicting the Impact on Human Emotion (Affect) Using Computing Systems
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    Chapter 40 Utilising User Texts to Improve Recommendations
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    Chapter 41 Semantically-Enhanced Ubiquitous User Modeling
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    Chapter 42 User Modeling Based on Emergent Domain Semantics
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    Chapter 43 “Biographic spaces”: A Personalized Smoking Cessation Intervention in Second Life
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    Chapter 44 Task-Based User Modelling for Knowledge Work Support
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    Chapter 45 Enhancing User Interaction in Virtual Environments through Adaptive Personalized 3D Interaction Techniques
Overall attention for this book and its chapters
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (64th percentile)
  • Good Attention Score compared to outputs of the same age and source (67th percentile)

Mentioned by

1 patent
1 Facebook page


6 Dimensions

Readers on

7 Mendeley
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User Modeling, Adaptation, and Personalization
Published by
ADS, June 2010
DOI 10.1007/978-3-642-13470-8
978-3-64-213469-2, 978-3-64-213470-8

Bra, Paul, Kobsa, Alfred, Chin, David

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer 1 14%
Unknown 6 86%
Readers by discipline Count As %
Computer Science 1 14%
Unknown 6 86%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 23 October 2018.
All research outputs
of 23,028,364 outputs
Outputs from ADS
of 37,456 outputs
Outputs of similar age
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Outputs of similar age from ADS
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Altmetric has tracked 23,028,364 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 37,456 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done well, scoring higher than 75% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 96,693 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.
We're also able to compare this research output to 381 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.