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Author: Touhid Bhuiyan
Publisher: Springer Science & Business Media
ISBN: 1461468957
Category : Computers
Languages : en
Pages : 123
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Book Description
Recommender systems are one of the recent inventions to deal with the ever-growing information overload in relation to the selection of goods and services in a global economy. Collaborative Filtering (CF) is one of the most popular techniques in recommender systems. The CF recommends items to a target user based on the preferences of a set of similar users known as the neighbors, generated from a database made up of the preferences of past users. In the absence of these ratings, trust between the users could be used to choose the neighbor for recommendation making. Better recommendations can be achieved using an inferred trust network which mimics the real world “friend of a friend” recommendations. To extend the boundaries of the neighbor, an effective trust inference technique is required. This book proposes a trust interference technique called Directed Series Parallel Graph (DSPG) that has empirically outperformed other popular trust inference algorithms, such as TidalTrust and MoleTrust. For times when reliable explicit trust data is not available, this book outlines a new method called SimTrust for developing trust networks based on a user’s interest similarity. To identify the interest similarity, a user’s personalized tagging information is used. However, particular emphasis is given in what resources the user chooses to tag, rather than the text of the tag applied. The commonalities of the resources being tagged by the users can be used to form the neighbors used in the automated recommender system. Through a series of case studies and empirical results, this book highlights the effectiveness of this tag-similarity based method over the traditional collaborative filtering approach, which typically uses rating data. Trust for Intelligent Recommendation is intended for practitioners as a reference guide for developing improved, trust-based recommender systems. Researchers in a related field will also find this book valuable.
Author: Touhid Bhuiyan
Publisher: Springer Science & Business Media
ISBN: 1461468957
Category : Computers
Languages : en
Pages : 123
Get Book Here
Book Description
Recommender systems are one of the recent inventions to deal with the ever-growing information overload in relation to the selection of goods and services in a global economy. Collaborative Filtering (CF) is one of the most popular techniques in recommender systems. The CF recommends items to a target user based on the preferences of a set of similar users known as the neighbors, generated from a database made up of the preferences of past users. In the absence of these ratings, trust between the users could be used to choose the neighbor for recommendation making. Better recommendations can be achieved using an inferred trust network which mimics the real world “friend of a friend” recommendations. To extend the boundaries of the neighbor, an effective trust inference technique is required. This book proposes a trust interference technique called Directed Series Parallel Graph (DSPG) that has empirically outperformed other popular trust inference algorithms, such as TidalTrust and MoleTrust. For times when reliable explicit trust data is not available, this book outlines a new method called SimTrust for developing trust networks based on a user’s interest similarity. To identify the interest similarity, a user’s personalized tagging information is used. However, particular emphasis is given in what resources the user chooses to tag, rather than the text of the tag applied. The commonalities of the resources being tagged by the users can be used to form the neighbors used in the automated recommender system. Through a series of case studies and empirical results, this book highlights the effectiveness of this tag-similarity based method over the traditional collaborative filtering approach, which typically uses rating data. Trust for Intelligent Recommendation is intended for practitioners as a reference guide for developing improved, trust-based recommender systems. Researchers in a related field will also find this book valuable.
Author: Stephen M.R. Covey
Publisher: Simon and Schuster
ISBN: 1451651473
Category : Business & Economics
Languages : en
Pages : 320
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Book Description
After illustrating the global relevance of trust with his book The Speed of Trust by selling more than one million copies in twenty-two languages, Stephen M.R. Covey again illuminates the hidden power of trust to change lives and impact organizations in Smart Trust. In a compelling and readable style, he and long-time business partner Greg Link share enlightening principles and anecdotes of people and organizations that are not only achieving unprecedented prosperity from high-trust relationships and cultures but—even more inspiring—also attaining elevated levels of energy and joy. Find out why trusted people are more likely to get hired or promoted, get the best projects and bigger budgets, and are last to be laid off. This sea-changing book will forever shift your perspective as it reveals and validates, once and for all, the transformational power of trust. Reading Smart Trust will increase your probability of thriving in this increasingly unpredictable marketplace. The more unpredictable it becomes, the more your (and your organization’s) sound judgment and ability to trust in this low-trust world will give you a tremendous competitive advantage—and the capacity to navigate the uncertainty low trust creates.
Author: Patricia Victor
Publisher: Springer Science & Business Media
ISBN: 9491216082
Category : Computers
Languages : en
Pages : 210
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Book Description
This book describes research performed in the context of trust/distrust propagation and aggregation, and their use in recommender systems. This is a hot research topic with important implications for various application areas. The main innovative contributions of the work are: -new bilattice-based model for trust and distrust, allowing for ignorance and inconsistency -proposals for various propagation and aggregation operators, including the analysis of mathematical properties -Evaluation of these operators on real data, including a discussion on the data sets and their characteristics. -A novel approach for identifying controversial items in a recommender system -An analysis on the utility of including distrust in recommender systems -Various approaches for trust based recommendations (a.o. base on collaborative filtering), an in depth experimental analysis, and proposal for a hybrid approach -Analysis of various user types in recommender systems to optimize bootstrapping of cold start users.
Author: Jennifer Golbeck
Publisher: Springer Science & Business Media
ISBN: 1848003560
Category : Computers
Languages : en
Pages : 335
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Book Description
This book has evolved out of roughly ve years of working on computing with social trust. In the beginning, getting people to accept that social networks and the relationships in them could be the basis for interesting, relevant, and exciting c- puter science was a struggle. Today, social networking and social computing have become hot topics, and those of us doing research in this space are nally nding a wealth of opportunities to share our work and to collaborate with others. This book is a collection of chapters that cover all the major areas of research in this space. I hope it will serve as a guide to students and researchers who want a strong introduction to work in the eld, and as encouragement and direction for those who are considering bringing their own techniques to bear on some of these problems. It has been an honor and privilege to work with these authors for whom I have so much respect and admiration. Thanks to all of them for their outstanding work, which speaks for itself, and for patiently enduringall my emails. Thanks, as always, to Jim Hendler for his constant support. Cai Ziegler has been particularly helpful, both as a collaborator, and in the early stages of development for this book. My appreciation also goes to Beverley Ford, Rebecca Mowat and everyone at Springer who helped with publication of this work.
Author: Sachi Nandan Mohanty
Publisher: John Wiley & Sons
ISBN: 1119711576
Category : Computers
Languages : en
Pages : 448
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Book Description
This book is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. It comprehensively covers the topic of recommender systems, which provide personalized recommendations of items or services to the new users based on their past behavior. Recommender system methods have been adapted to diverse applications including social networking, movie recommendation, query log mining, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. Recommendations in agricultural or healthcare domains and contexts, the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. This book illustrates how this technology can support the user in decision-making, planning and purchasing processes in agricultural & healthcare sectors.
Author: Michael D. Ekstrand
Publisher: Now Publishers Inc
ISBN: 1601984421
Category : Computers
Languages : en
Pages : 104
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Book Description
Collaborative Filtering Recommender Systems discusses a wide variety of the recommender choices available and their implications, providing both practitioners and researchers with an introduction to the important issues underlying recommenders and current best practices for addressing these issues.
Author: Katarzyna M. Wegrzyn-Wolska
Publisher: Springer Science & Business Media
ISBN: 3540725741
Category : Computers
Languages : en
Pages : 413
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Book Description
This book contains papers presented at the 5th Atlantic Web Intelligence Conference, AWIC’2007, held in Fontainbleau, France, in June 2007, and organized by Esigetel, Technical University of Lodz, and Polish Academy of Sciences. It includes reports from the front of diverse fields of the Web, including application of artificial intelligence, design, information retrieval and interpretation, user profiling, security, and engineering.
Author: Alireza Rezvanian
Publisher: Springer
ISBN: 3030107671
Category : Technology & Engineering
Languages : en
Pages : 339
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Book Description
This book begins by briefly explaining learning automata (LA) models and a recently developed cellular learning automaton (CLA) named wavefront CLA. Analyzing social networks is increasingly important, so as to identify behavioral patterns in interactions among individuals and in the networks’ evolution, and to develop the algorithms required for meaningful analysis. As an emerging artificial intelligence research area, learning automata (LA) has already had a significant impact in many areas of social networks. Here, the research areas related to learning and social networks are addressed from bibliometric and network analysis perspectives. In turn, the second part of the book highlights a range of LA-based applications addressing social network problems, from network sampling, community detection, link prediction, and trust management, to recommender systems and finally influence maximization. Given its scope, the book offers a valuable guide for all researchers whose work involves reinforcement learning, social networks and/or artificial intelligence.
Author: Zeeshan-Ul-Hassan Usmani
Publisher: BoD – Books on Demand
ISBN: 9537619850
Category : Computers
Languages : en
Pages : 496
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Book Description
This book presents a unique and diversified collection of research work ranging from controlling the activities in virtual world to optimization of productivity in games, from collaborative recommendations to populate an open computational environment with autonomous hypothetical reasoning, and from dynamic health portal to measuring information quality, correctness, and readability from the web.
Author: Xin Liu
Publisher: CRC Press
ISBN: 1482226669
Category : Computers
Languages : en
Pages : 234
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Book Description
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.
