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Behavior Computing : Modeling, Analysis, Mining and Decision / edited by Longbing Cao, Philip S. Yu
Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: SpringerLink BücherPublisher: London : Springer London, 2012Description: Online-Ressource (XVI, 374p. 122 illus, digital)ISBN:- 9781447129691
- 1280793058
- 9781280793059
- 005.743
- 302.23/1 302.231
- QA75.5-76.95
Contents:
Summary: Philip S. YuSummary: 'Behavior' is an increasingly important concept in the scientific, societal, economic, cultural, political, military, living and virtual worlds. Behavior computing, or behavior informatics, consists of methodologies, techniques and practical tools for examining and interpreting behaviours in these various worlds. Behavior computing contributes to the in-depth understanding, discovery, applications and management of behavior intelligence. With contributions from leading researchers in this emerging field Behavior Computing: Modeling, Analysis, Mining and Decision includes chapters on: representation and modeling behaviors; behavior ontology; behaviour analysis; behaviour pattern mining; clustering complex behaviors; classification of complex behaviors; behaviour impact analysis; social behaviour analysis; organizational behaviour analysis; and behaviour computing applications. Behavior Computing: Modeling, Analysis, Mining and Decision provides a dedicated source of reference for the theory and applications of behavior informatics and behavior computing. Researchers, research students and practitioners in behavior studies, including computer science, behavioral science, and social science communities will find this state of the art volume invaluable.PPN: PPN: 1651468729Package identifier: Produktsigel: ZDB-2-SCS
Behavior Computing; Preface; Contents; Contributors; Part I: Behavior Modeling; Chapter 1: Analyzing Behavior of the Influentials Across Social Media; 1.1 Introduction; 1.2 Social Media Taxonomy; 1.3 Influence in Social Media; 1.4 Data Collection; 1.4.1 Advantages of Cross-Site Information; 1.4.2 Challenges of Collecting Cross-Site Information; 1.5 Studying Influentials Across Sites; 1.5.1 Sustenance of Influence; 1.5.2 Sphere of Influence; 1.5.3 Influence Homophily Across Different Sites; 1.5.4 Observed Influential Behavioral Patterns; 1.6 Conclusions; References
Chapter 2: Modeling and Analysis of Social Activity Process2.1 Introduction; 2.2 Behavior Model; 2.2.1 Actor Sub-model; 2.2.2 Action Sub-model; 2.2.3 Environment Sub-model; 2.2.4 Relationship Sub-model; 2.3 Behavior Property; 2.4 Behavior Analysis; 2.5 Case Study; 2.6 Conclusion; References; Chapter 3: Behaviour Representation and Management Making Use of the Narrative Knowledge Representation Language; 3.1 Introduction; 3.2 A Short Presentation of the NKRL Language; 3.2.1 General Principles; 3.2.2 Additional Details; 3.3 "Behavioural" Information and the NKRL Templates
3.3.1 "Behave:" TemplatesThe Behave:HumanProperty Templates; Acting to Obtain a Given Result; The Behave:Attitude Templates; 3.3.2 "Behavioural" Aspects in the Templates of the Residual HTemp Branches; The Exist: Templates; The Experience: Templates; The Move: Templates; The Own: Templates; The Produce: Templates; The Receive: Templates; 3.4 The Query/Inference Aspects; 3.5 Related Work; 3.6 Conclusion; References; Chapter 4: Semi-Markovian Representation of User Behavior in Software Packages; 4.1 Introduction; 4.1.1 Objectives of the Present Work
4.2 In Defence of Semi-Markovian Representation of User Behavior4.3 Modeling User Behavior; 4.3.1 Computing User Behavioral Statistics; Holding Times; Transition Probabilities; 4.4 ActiveX Servers and Controls-A Demo Software Package; 4.5 Testing; 4.5.1 Organization of the User Testing of the Package; 4.5.2 The User Behavior Database; 4.5.3 Evaluation Metrics for Personalization; 4.6 Simulation Model of User Behavior; 4.6.1 Data for the Simulation Model; 4.6.2 Development of the Model; 4.6.3 Analysis of the Simulation Results; 4.7 Conclusions; References; Part II: Behavior Analysis
Chapter 5: P-SERS: Personalized Social Event Recommender System5.1 Introduction; 5.2 Preliminaries; 5.2.1 Problem Description; 5.2.2 Related Works; Group Buying Communities; Social Information Filtering; 5.3 System Model of P-SERS; 5.3.1 Candidate Selection; User Profile Collection; Social Graph Construction; 5.3.2 Social Measurement; Initiator Score; Participant Score; Target Score; 5.3.3 Recommendation; Explanations; Grouping; 5.4 Experiments; 5.4.1 Dataset and User Understanding; 5.4.2 Recommendation Satisfaction; 5.4.3 Explanation Effectiveness; 5.5 Conclusions; References
Chapter 6: Simultaneously Modeling Reply Networks and Contents to Generate User's Profiles on Web Forum
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