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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 Social Computers for the Social Animal: State-of-the-Art and Future Perspectives of Social Signal Processing
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    Chapter 2 Thinking Outside the (Search) Box
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    Chapter 3 Challenges for the Multi-dimensional Personalised Web
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    Chapter 4 Modeling User Affect from Causes and Effects
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    Chapter 5 Evaluating Web Based Instructional Models Using Association Rule Mining
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    Chapter 6 Sensors Model Student Self Concept in the Classroom
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    Chapter 7 Use and Trust of Simple Independent Open Learner Models to Support Learning within and across Courses
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    Chapter 8 Narcissus: Group and Individual Models to Support Small Group Work
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    Chapter 9 Social Navigation Support for Information Seeking: If You Build It, Will They Come?
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    Chapter 10 Performance Evaluation of a Privacy-Enhancing Framework for Personalized Websites
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    Chapter 11 Creating User Profiles from a Command-Line Interface: A Statistical Approach
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    Chapter 12 Context-Aware Preference Model Based on a Study of Difference between Real and Supposed Situation Data
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    Chapter 13 Modeling the Personality of Participants During Group Interactions
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    Chapter 14 Predicting Customer Models Using Behavior-Based Features in Shops
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    Chapter 15 Investigating the Utility of Eye-Tracking Information on Affect and Reasoning for User Modeling
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    Chapter 16 Describing User Interactions in Adaptive Interactive Systems
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    Chapter 17 PerspectiveSpace: Opinion Modeling with Dimensionality Reduction
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    Chapter 18 Recognition of User Intentions for Interface Agents with Variable Order Markov Models
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    Chapter 19 Tell Me Where You’ve Lived, and I’ll Tell You What You Like: Adapting Interfaces to Cultural Preferences
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    Chapter 20 Non-intrusive Personalisation of the Museum Experience
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    Chapter 21 Assessing the Impact of Measurement Uncertainty on User Models in Spatial Domains
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    Chapter 22 SoNARS: A Social Networks-Based Algorithm for Social Recommender Systems
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    Chapter 23 Grocery Product Recommendations from Natural Language Inputs
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    Chapter 24 I Like It... I Like It Not: Evaluating User Ratings Noise in Recommender Systems
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    Chapter 25 Evaluating Interface Variants on Personality Acquisition for Recommender Systems
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    Chapter 26 Context-Dependent Personalised Feedback Prioritisation in Exploratory Learning for Mathematical Generalisation
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    Chapter 27 Google Shared. A Case-Study in Social Search
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    Chapter 28 Collaborative Filtering Is Not Enough? Experiments with a Mixed-Model Recommender for Leisure Activities
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    Chapter 29 Enhancing Mobile Recommender Systems with Activity Inference
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    Chapter 30 Customer’s Relationship Segmentation Driving the Predictive Modeling for Bad Debt Events
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    Chapter 31 Supporting Personalized User Concept Spaces and Recommendations for a Publication Sharing System
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    Chapter 32 Evaluating the Adaptation of a Learning System before the Prototype Is Ready: A Paper-Based Lab Study
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    Chapter 33 Capturing the User’s Reading Context for Tailoring Summaries
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    Chapter 34 History Dependent Recommender Systems Based on Partial Matching
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    Chapter 35 Capturing User Intent for Analytic Process
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    Chapter 36 What Have the Neighbours Ever Done for Us? A Collaborative Filtering Perspective
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    Chapter 37 Investigating the Possibility of Adaptation and Personalization in Virtual Environments
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    Chapter 38 Detecting Guessed and Random Learners’ Answers through Their Brainwaves
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    Chapter 39 Just-in-Time Adaptivity through Dynamic Items
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    Chapter 40 Collaborative Semantic Tagging of Web Resources on the Basis of Individual Knowledge Networks
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    Chapter 41 Working Memory Differences in E-Learning Environments: Optimization of Learners’ Performance through Personalization
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    Chapter 42 Semantic Web Usage Mining: Using Semantics to Understand User Intentions
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    Chapter 43 Adaptive Tips for Helping Domain Experts
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    Chapter 44 On User Modelling for Personalised News Video Recommendation
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    Chapter 45 A Model of Temporally Changing User Behaviors in a Deployed Spoken Dialogue System
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    Chapter 46 Recognition of Users’ Activities Using Constraint Satisfaction
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    Chapter 47 Reinforcing Recommendation Using Implicit Negative Feedback
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    Chapter 48 Evaluating Three Scrutability and Three Privacy User Privileges for a Scrutable User Modelling Infrastructure
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    Chapter 49 User Modeling of Disabled Persons for Generating Instructions to Medical First Responders
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    Chapter 50 Filtering Fitness Trail Content Generated by Mobile Users
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    Chapter 51 Adaptive Clustering of Search Results
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    Chapter 52 What Do Academic Users Really Want from an Adaptive Learning System?
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    Chapter 53 How Users Perceive and Appraise Personalized Recommendations
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    Chapter 54 Towards Web Usability: Providing Web Contents According to the Readers Contexts
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    Chapter 55 Plan Recognition of Movement
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    Chapter 56 Personalised Web Experiences: Seamless Adaptivity across Web Service Composition and Web Content
Attention for Chapter 47: Reinforcing Recommendation Using Implicit Negative Feedback
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Chapter title
Reinforcing Recommendation Using Implicit Negative Feedback
Chapter number 47
Book title
User Modeling, Adaptation, and Personalization
Published by
Springer Berlin Heidelberg, June 2009
DOI 10.1007/978-3-642-02247-0_47
Book ISBNs
978-3-64-202246-3, 978-3-64-202247-0
Authors

Danielle H. Lee, Peter Brusilovsky

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Slovakia 2 5%
Brazil 2 5%
Austria 1 2%
Netherlands 1 2%
Germany 1 2%
China 1 2%
Russia 1 2%
Spain 1 2%
United States 1 2%
Other 0 0%
Unknown 32 74%

Demographic breakdown

Readers by professional status Count As %
Student > Master 13 30%
Student > Ph. D. Student 10 23%
Researcher 5 12%
Professor > Associate Professor 3 7%
Student > Doctoral Student 2 5%
Other 5 12%
Unknown 5 12%
Readers by discipline Count As %
Computer Science 32 74%
Arts and Humanities 2 5%
Business, Management and Accounting 1 2%
Psychology 1 2%
Engineering 1 2%
Other 0 0%
Unknown 6 14%