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05711nam a22005775i 4500 |
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978-3-642-13470-8 |
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DE-He213 |
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20151204182514.0 |
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|a 9783642134708
|9 978-3-642-13470-8
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|a 10.1007/978-3-642-13470-8
|2 doi
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|a QA76.9.D343
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|a COM021030
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|a 006.312
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|a User Modeling, Adaptation, and Personalization
|h [electronic resource] :
|b 18th International Conference, UMAP 2010, Big Island, HI, USA, June 20-24, 2010. Proceedings /
|c edited by Paul De Bra, Alfred Kobsa, David Chin.
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg,
|c 2010.
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|a XVIII, 428 p. 115 illus.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
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|a online resource
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|a text file
|b PDF
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|a Lecture Notes in Computer Science,
|x 0302-9743 ;
|v 6075
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|a Keynote Speakers -- Modeling Emotion and Its Expression in Virtual Humans -- AdHeat — An Influence-Based Diffusion Model for Propagating Hints to Personalize Social Ads -- Full Research Papers -- Can Concept-Based User Modeling Improve Adaptive Visualization? -- Interweaving Public User Profiles on the Web -- Modeling Long-Term Search Engine Usage -- Analysis of Strategies for Building Group Profiles -- Contextual Slip and Prediction of Student Performance after Use of an Intelligent Tutor -- Working Memory Span and E-Learning: The Effect of Personalization Techniques on Learners’ Performance -- Scaffolding Self-directed Learning with Personalized Learning Goal Recommendations -- Instructional Video Content Employing User Behavior Analysis: Time Dependent Annotation with Levels of Detail -- A User-and Item-Aware Weighting Scheme for Combining Predictive User Models -- PersonisJ: Mobile, Client-Side User Modelling -- Twitter, Sensors and UI: Robust Context Modeling for Interruption Management -- Ranking Feature Sets for Emotion Models Used in Classroom Based Intelligent Tutoring Systems -- Inducing Effective Pedagogical Strategies Using Learning Context Features -- “Yes!”: Using Tutor and Sensor Data to Predict Moments of Delight during Instructional Activities -- A Personalized Graph-Based Document Ranking Model Using a Semantic User Profile -- Interaction and Personalization of Criteria in Recommender Systems -- Collaborative Inference of Sentiments from Texts -- User Modelling for Exclusion and Anomaly Detection: A Behavioural Intrusion Detection System -- IntrospectiveViews: An Interface for Scrutinizing Semantic User Models -- Analyzing Community Knowledge Sharing Behavior -- A Data-Driven Technique for Misconception Elicitation -- Modeling Individualization in a Bayesian Networks Implementation of Knowledge Tracing -- Detecting Gaming the System in Constraint-Based Tutors -- Bayesian Credibility Modeling for Personalized Recommendation in Participatory Media -- A Study on User Perception of Personality-Based Recommender Systems -- Compass to Locate the User Model I Need: Building the Bridge between Researchers and Practitioners in User Modeling -- Industry Papers -- myCOMAND Automotive User Interface: Personalized Interaction with Multimedia Content Based on Fuzzy Preference Modeling -- User Modeling for Telecommunication Applications: Experiences and Practical Implications -- Mobile Web Profiling: A Study of Off-Portal Surfing Habits of Mobile Users -- Personalized Implicit Learning in a Music Recommender System -- Short Research Papers -- Personalised Pathway Prediction -- Towards a Customization of Rating Scales in Adaptive Systems -- Eye-Tracking Study of User Behavior in Recommender Interfaces -- Recommending Food: Reasoning on Recipes and Ingredients -- Disambiguating Search by Leveraging a Social Context Based on the Stream of User’s Activity -- Features of an Independent Open Learner Model Influencing Uptake by University Students -- Doctoral Consortium Papers -- Recognizing and Predicting the Impact on Human Emotion (Affect) Using Computing Systems -- Utilising User Texts to Improve Recommendations -- Semantically-Enhanced Ubiquitous User Modeling -- User Modeling Based on Emergent Domain Semantics -- “Biographic spaces”: A Personalized Smoking Cessation Intervention in Second Life -- Task-Based User Modelling for Knowledge Work Support -- Enhancing User Interaction in Virtual Environments through Adaptive Personalized 3D Interaction Techniques.
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|a Computer science.
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|a Database management.
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|a Data mining.
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|a Information storage and retrieval.
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|a User interfaces (Computer systems).
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|a Artificial intelligence.
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|a Computer Science.
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|a Data Mining and Knowledge Discovery.
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|a Information Systems Applications (incl. Internet).
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|a Database Management.
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|a Information Storage and Retrieval.
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|a Artificial Intelligence (incl. Robotics).
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|a User Interfaces and Human Computer Interaction.
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|a Bra, Paul De.
|e editor.
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|a Kobsa, Alfred.
|e editor.
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|a Chin, David.
|e editor.
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|a SpringerLink (Online service)
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|t Springer eBooks
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|i Printed edition:
|z 9783642134692
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|a Lecture Notes in Computer Science,
|x 0302-9743 ;
|v 6075
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|u http://dx.doi.org/10.1007/978-3-642-13470-8
|z Full Text via HEAL-Link
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|a ZDB-2-SCS
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|a ZDB-2-LNC
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|a Computer Science (Springer-11645)
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