Computational Trust Models and Machine Learning

Автор: Liu, Xin (Mathematician); Datta, Anwitaman; Lim, Ee-Peng
Название: Computational Trust Models and Machine Learning
Язык: английский
Издательство: Boca Raton, FL: CRC Press
Год: 2015
Объем: 232 p.
Серия: Chapman & Hall/CRC machine learning & pattern recognition series
Формат: pdf
Размер: 13 mb
Xin Liu is currently a postdoctoral researcher in the Laboratoire de Systèmes d'Informations Répartis, led by Professor Karl Aberer, at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. Before joining EPFL, Xin received his Ph.D in computer science from Nanyang Technological University in Singapore, supervised by Associate Professor Anwitaman Datta. His current research interests include recommender systems, trust and reputation systems, social computing, and distributed computing. His papers have been accepted at several prestigious academic events, and he has been a program committee member and reviewer for numerous international conferences and journals.
Anwitaman Datta is an associate professor at Nanyang Technological University, Singapore, where he leads the Self- Aspects of Networked and Distributed Systems Research Group and teaches courses on security management and cryptography and network security. Well published, he has focused his research on P2P storage, decentralized online social networks, structured overlays, and computational trust. His current research interests include the design of resilient large-scale distributed systems, coding for storage, security and privacy, and social media analysis. His projects have been funded by the Singapore Ministry of Education, HP Labs Innovation Research Award, and more.
Ee-Peng Lim is a professor at Singapore Management University (SMU), co-director of the SMU/Carnegie Mellon University Living Analytics Research Center, and associate editor of numerous journals and publications. He holds a Ph.D from the University of Minnesota, Minneapolis, USA and a B.Sc from the National University of Singapore. His current research interests include social network and web mining, information integration, and digital libraries. A former ACM Publications Board member, he currently serves on the steering committees of the International Conference on Asian Digital Libraries, Pacific Asia Conference on Knowledge Discovery and Data Mining, and International Conference on Social Informatics.
Computational Trust Models and Machine Learning provides a detailed introduction to the concept of trust and its application in various computer science areas, including multi-agent systems, online social networks, and communication systems. Identifying trust modeling challenges that cannot be addressed by traditional approaches, this book:
Explains how reputation-based systems are used to determine trust in diverse online communities
Describes how machine learning techniques are employed to build robust reputation systems
Explores two distinctive approaches to determining credibility of resources—one where the human role is implicit, and one that leverages human input explicitly
Shows how decision support can be facilitated by computational trust models
Discusses collaborative filtering-based trust aware recommendation systems
Defines a framework for translating a trust modeling problem into a learning problem
Investigates the objectivity of human feedback, emphasizing the need to filter out outlying opinions
Computational Trust Models and Machine Learning effectively demonstrates how novel machine learning techniques can improve the accuracy of trust assessment.
 CONTENT:
List of Figures
List of Tables
Preface
About the Editors
Contributors
1 Introduction
1.1 Overview........................................
1.2 What Is Trust?..................................
1.3 Computational Trust ............................
1.3.1 Computational Trust Modeling: A Review . .
1.3.1.1 Summation and Average............
1.3.1.2 Bayesian Inference...............
1.3.1.3 Web of Trust.....................
1.3.1.4 Iterative Methods................
1.3.2 Machine Learning for Trust Modeling ....
1.3.2.1 A Little Bit about Machine Learning
1.3.2.2 Machine Learning for Trust.......
1.4 Structure of the Book...........................
2 Trust in Online Communities
2.1 Introduction ...................................
2.2 Trust in E-Commerce Environments................
2.3 Trust in Search Engines.........................
2.4 Trust in P2P Information Sharing Networks ....
2.5 Trust in Service-Oriented Environments..........
2.6 Trust in Social Networks .......................
2.7 Discussion .....................................
3 Judging the Veracity of Claims and Reliability of Sources
3.1 Introduction .............................................
3.2 Related Work ............................................
3.2.1 Foundations of TYust...............................
3.2.2 Consistency in Information Extraction..............
3.2.2.1 Local Consistency.........................
3.2.2.2 Global Consistency .......................
3.2.3 Source Dependence..................................
3.2.3.1 Comparison to Credibility Analysis........
3.2.4 Comparison to Other Trust Mechanisms...............
3.3 Fact-Finding ............................................
3.3.1 Priors.............................................
3.3.2 Fact-Finding Algorithms............................
3.3.2.1 Sums (Hubs and Authorities)...............
3.3.2.2 Average-Log...............................
3.3.2.3 Investment................................
3.3.2.4 Pooledlnvestment..........................
3.3.2.5 TruthFinder...............................
3.3.2.6 3-Estimates...............................
3.4 Generalized Constrained Fact-Finding.....................
3.5 Generalized Fact-Finding ................................
3.5.1 Rewriting Fact-Finders for Assertion Weights.......
3.5.1.1 Generalized Sums (Hubs and Authorities) . .
3.5.1.2 Generalized Average-Log...................
3.5.1.3 Generalized Investment....................
3.5.1.4 Generalized Pooledlnvestment..............
3.5.1.5 Generalized TruthFinder...................
3.5.1.6 Generalized 3-Estimates...................
3.5.2 Encoding Information in Weighted Assertions........
3.5.2.1 Uncertainty in Information Extraction . . . .
3.5.2.2 Uncertainty of the Source.................
3.5.2.3 Similarity between Claims ................
3.5.2.4 Group Membership via Weighted Assertions
3.5.3 Encoding Groups and Attributes as Layers of Graph Nodes..............................................
3.5.3.1 Source Domain Expertise...................
3.5.3.2 Additional Layers versus Weighted Edges .....
3.6 Constrained Fact-Finding.................................
3.6.1 Propositional Linear Programming...................
3.6.2 Cost Function......................................
3.6.3 Values —> Votes —> Belief..........................
3.6.4 LP Decomposition...................................
3.6.5 Tie Breaking.......................................
3.6.6 “Unknown" Augmentation ............................
3.7 Experimental Results .......................................
3.7.1 Data................................................
3.7.1.1 Population.................................
3.7.1.2 Books......................................
3.7.1.3 Biography .................................
3.7.1.4 American vs. British Spelling..............
3.7.2 Experimental Setup..................................
3.7.3 Generalized Fact-Finding............................
3.7.3.1 Tuned Assertion Certainty..................
3.7.3.2 Uncertainty in Information Extraction ....
3.7.3.3 Groups as Weighted Assertions..............
3.7.3.4 Groups as Additional Layers................
3.7.4 Constrained Fact-Finding............................
3.7.4.1 IBT vs. L+I................................
3.7.4.2 City Population............................
3.7.4.3 Synthetic City Population..................
3.7.4.4 Basic Biographies..........................
3.7.4.5 American vs. British Spelling..............
3.7.5 The Joint Generalized Constrained Fact-Finding Framework ......................................................
3.8 Conclusion .................................................
4 Web Credibility Assessment
4.1 Introduction ...............................................
4.2 Web Credibility Overview....................................
4.2.1 What Is Web Credibility?............................
4.2.2 Introduction to Research on Credibility.............
4.2.3 Current Research....................................
4.2.4 Definitions Used in This Chapter....................
4.2.4.1 Information Credibility....................
4.2.4.2 Information Controversy....................
4.2.4.3 Credibility Support for Various Types of Information ..........................................
4.3 Data Collection ............................................
4.3.1 Collection Means....................................
4.3.1.1 Existing Datasets..........................
4.3.1.2 Data from Tools Supporting Credibility Evaluation .............................................
4.3.1.3 Data from Labelers.........................
4.3.2 Supporting Web Credibility Evaluation...............
4.3.2.1 Support User’s Expertise...................
4.3.2.2 Crowdsourcing Systems......................
4.3.2.3 Databases, Search Engines, Antiviruses and Lists of Pre-Scanned Sites................
4.3.2.4 Certification, Signatures and Seals.......
4.3.3 Reconcile - A Case Study..........................
4.4 Analysis of Content Credibility Evaluations .............
4.4.1 Subjectivity......................................
4.4.2 Consensus and Controversy.........................
4.4.3 Cognitive Bias....................................
4.4.3.1 Omnipresent Negative Skew - Shift Towards Positive.................................
4.4.3.2 Users Characteristics Affecting Credibility Evaluation Selected Personality Traits ....
4.4.3.3 Users Characteristics Affecting Credibility Evaluation - Cognitive Heuristics........
4.5 Aggregation Methods - What Is The Overall Credibility? ...
4.5.1 How to Measure Credibility........................
4.5.2 Standard Aggregates...............................
4.5.3 Combating Bias - Whose Vote Should Count More? ......
4.6 Classifying Credibility Evaluations Using External Web Content Features.........................................
4.6.1 How We Get Values of Outcome Variable.............
4.6.2 Motivation for Building a Feature-Based Classifier of Webpages Credibility .............................
4.6.3 Classification of Web Pages Credibility - Related Work........................
4.6.4 Dealing with Controversy Problem..................
4.6.5 Aggregation of Evaluations........................
4.6.6 Features..........................................
4.6.7 Results of Experiments with Building of Classifier Determining whether a Webpage Is Highly Credible (HC), Neutral (N) or Highly Not Credible (HNC)..........
4.6.8 Results of Experiments with Build of Binary Classifier Determining whether Webpage Is Credible or Not ..........
4.6.9 Results of Experiments with Build of Binary Classifier of Controversy....................................
4.6.10 Summary and Improvement Suggestions..............
5 Trust-Aware Recommender Systems
5.1 Recommender Systems .....................................
5.1.1 Content-Based Recommendation......................
5.1.2 Collaborative Filtering (CF) .....................
5.1.2.1 Memory-Based Collaborative Filtering ....
5.1.2.2 Model-Based Collaborative Filtering.......
5.1.3 Hybrid Recommendation ............................
5.1.4 Evaluating Recommender Systems....................
5.1.5 Challenges of Recommender Systems.................
5.1.5.1 Cold Start................................
5.1.5.2 Data Sparsity ...........................
5.1.5.3 Attacks..................................
5.1.6 Summary...........................................
5.2 Computational Models of Trust in Recommender Systems . .
5.2.1 Definition and Properties........................
5.2.1.1 Notations................................
5.2.1.2 Trust Networks...........................
5.2.1.3 Properties of Trust......................
5.2.2 Global and Local Trust Metrics...................
5.2.3 Inferring Trust Values...........................
5.2.3.1 Inferring Trust in Binary Trust Networks . .
5.2.3.2 Inferring Trust in Continuous Trust Networks
5.2.3.3 Inferring Implicit Trust Values..........
5.2.3.4 Trust Aggregation........................
5.2.4 Summary..........................................
5.3 Incorporating Trust in Recommender Systems .............
5.3.1 Trust-Aware Memory-Based CF Systems .............
5.3.1.1 Trust-A ware Filtering...................
5.3.1.2 Trust-A ware Weighting...................
5.3.2 Trust-Aware Model-Based CF Systems...............
5.3.3 Recommendation Using Distrust Information........
5.3.4 Advantages of Trust-Aware Recommendation.........
5.3.5 Research Directions of Trust-Aware Recommendation
5.4 Conclusion .............................................
6 Biases in Trust-Based Systems
6.1 Introduction ...........................................
6.2 Types of Biases ........................................
6.2.1 Cognitive Bias...................................
6.2.2 Spam ............................................
6.3 Detection of Biases ....................................
6.3.1 Unsupervised Approaches..........................
6.3.2 Supervised Approaches ...........................
6.4 Lessening the Impact of Biases..........................
6.4.1 Avoidance........................................
6.4.2 Aggregation......................................
6.4.3 Compensation ....................................
6.4.4 Elimination......................................
6.5 Summary.................................................
Bibliography
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