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This second edition of a well-received text, with 20 new chapters,
presents a coherent and unified repository of recommender systems'
major concepts, theories, methodologies, trends, and challenges. A
variety of real-world applications and detailed case studies are
included. In addition to wholesale revision of the existing
chapters, this edition includes new topics including: decision
making and recommender systems, reciprocal recommender systems,
recommender systems in social networks, mobile recommender systems,
explanations for recommender systems, music recommender systems,
cross-domain recommendations, privacy in recommender systems, and
semantic-based recommender systems. This multi-disciplinary
handbook involves world-wide experts from diverse fields such as
artificial intelligence, human-computer interaction, information
retrieval, data mining, mathematics, statistics, adaptive user
interfaces, decision support systems, psychology, marketing, and
consumer behavior. Theoreticians and practitioners from these
fields will find this reference to be an invaluable source of
ideas, methods and techniques for developing more efficient,
cost-effective and accurate recommender systems.
This third edition handbook describes in detail the classical
methods as well as extensions and novel approaches that were more
recently introduced within this field. It consists of five parts:
general recommendation techniques, special recommendation
techniques, value and impact of recommender systems, human computer
interaction, and applications. The first part presents the most
popular and fundamental techniques currently used for building
recommender systems, such as collaborative filtering,
semantic-based methods, recommender systems based on implicit
feedback, neural networks and context-aware methods. The
second part of this handbook introduces more advanced
recommendation techniques, such as session-based recommender
systems, adversarial machine learning for recommender systems,
group recommendation techniques, reciprocal recommenders systems,
natural language techniques for recommender systems and
cross-domain approaches to recommender systems. The third part
covers a wide perspective to the evaluation of recommender systems
with papers on methods for evaluating recommender systems, their
value and impact, the multi-stakeholder perspective of recommender
systems, the analysis of the fairness, novelty and diversity in
recommender systems. The fourth part contains a few chapters on the
human computer dimension of recommender systems, with research on
the role of explanation, the user personality and how to
effectively support individual and group decision with recommender
systems. The last part focusses on application in several important
areas, such as, food, music, fashion and multimedia
recommendation. This informative third edition handbook
provides a comprehensive, yet concise and convenient reference
source to recommender systems for researchers and advanced-level
students focused on computer science and data science.
Professionals working in data analytics that are using
recommendation and personalization techniques will also find this
handbook a useful tool.Â
This third edition handbook describes in detail the classical
methods as well as extensions and novel approaches that were more
recently introduced within this field. It consists of five parts:
general recommendation techniques, special recommendation
techniques, value and impact of recommender systems, human computer
interaction, and applications. The first part presents the most
popular and fundamental techniques currently used for building
recommender systems, such as collaborative filtering,
semantic-based methods, recommender systems based on implicit
feedback, neural networks and context-aware methods. The second
part of this handbook introduces more advanced recommendation
techniques, such as session-based recommender systems, adversarial
machine learning for recommender systems, group recommendation
techniques, reciprocal recommenders systems, natural language
techniques for recommender systems and cross-domain approaches to
recommender systems. The third part covers a wide perspective to
the evaluation of recommender systems with papers on methods for
evaluating recommender systems, their value and impact, the
multi-stakeholder perspective of recommender systems, the analysis
of the fairness, novelty and diversity in recommender systems. The
fourth part contains a few chapters on the human computer dimension
of recommender systems, with research on the role of explanation,
the user personality and how to effectively support individual and
group decision with recommender systems. The last part focusses on
application in several important areas, such as, food, music,
fashion and multimedia recommendation. This informative third
edition handbook provides a comprehensive, yet concise and
convenient reference source to recommender systems for researchers
and advanced-level students focused on computer science and data
science. Professionals working in data analytics that are using
recommendation and personalization techniques will also find this
handbook a useful tool.
This work is intended to be of interest to counter-terrorism
experts and professionals, to academic researchers in information
systems, computer science, political science, and public policy,
and to graduate students in these areas. The goal of this book is
to highlight several aspects of patrolling the Web that were raised
and discussed by experts from different disciplines. The book
includes academic studies from related technical fields, namely,
computer science, and information technology, the strategic point
of view as presented by intelligence experts, and finally the
practical point of view by experts from related industry describing
lessons learned from practical efforts to tackle these problems.
This volume is organized into four major parts: definition and
analysis of the subject, data-mining techniques for terrorism
informatics, other theoretical methods to detect terrorists on the
Web, and practical relevant industrial experience on patrolling the
Web.
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