Umfang:
Online-Ressource (1008 p)
Ausgabe:
2nd ed
ISBN:
9781489976369
Inhalt:
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. Francesco Ricci is a professor of computer science at the Free University of Bozen-Bolzano, Italy. His current research interests include recommender systems, intelligent interfaces, mobile systems, machine learning, case-based reasoning, and the applications of ICT to health and tourism. He has published more than one hundred thirty of academic papers on these topics. He is the editor in chief of the Journal of Information Technology Tourism and on the editorial board of User Modeling and User Adapted Interaction. Lior Rokach is a data scientist and an associate professor of information systems and software engineering at Ben-Gurion University of the Negev (BGU). Rokach established the machine learning laboratory in BGU which promotes innovative adaptations of machine learning and data mining methods to create the next generation of intelligent systems. Rokach is known for his contributions to the advancement of machine learning, recommender systems and cyber security. Bracha Shapira is an associate professor and the head of the information systems and engineering Department at Ben-Gurion University of the Negev (BGU). She leads large scale research projects at the Telekom Innovation Laboratories at BGU in the area of data analytics, recommender systems and personalization that delivers innovative technologies to address challenges in these fields. Shapira is known for her contribution in integrating social network, context awareness and privacy consideration to recommender systems.
Anmerkung:
Description based upon print version of record
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Preface; Contents; Contributors; 1 Recommender Systems: Introduction and Challenges; 1.1 Introduction; 1.2 Recommender Systems' Function; 1.3 Data and Knowledge Sources; 1.4 Recommendation Techniques; 1.5 Recommender Systems Evaluation; 1.6 Recommender Systems Applications; 1.7 Recommender Systems and Human Computer Interaction; 1.8 Advanced Topics; 1.9 Challenges; 1.9.1 Preference Acquisition and Profiling; 1.9.2 Interaction; 1.9.3 New Recommendation Tasks; References; Part I Recommendation Techniques; 2 A Comprehensive Survey of Neighborhood-Based Recommendation Methods; 2.1 Introduction
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2.1.1 Advantages of Neighborhood Approaches2.1.2 Objectives and Outline; 2.2 Problem Definition and Notation; 2.3 Neighborhood-Based Recommendation; 2.3.1 User-Based Rating Prediction; 2.3.2 User-Based Classification; 2.3.3 Regression vs Classification; 2.3.4 Item-Based Recommendation; 2.3.5 User-Based vs Item-Based Recommendation; 2.4 Components of Neighborhood Methods; 2.4.1 Rating Normalization; 2.4.1.1 Mean-Centering; 2.4.1.2 Z-Score Normalization; 2.4.1.3 Choosing a Normalization Scheme; 2.4.2 Similarity Weight Computation; 2.4.2.1 Correlation-Based Similarity
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2.4.2.2 Other Similarity Measures2.4.2.3 Considering the Significance of Weights; 2.4.2.4 Considering the Variance of Ratings; 2.4.2.5 Considering the Target Item; 2.4.3 Neighborhood Selection; 2.4.3.1 Pre-filtering of Neighbors; 2.4.3.2 Neighbors in the Predictions; 2.5 Advanced Techniques; 2.5.1 Graph-Based Methods; 2.5.1.1 Path-Based Similarity; 2.5.1.2 Random Walk Similarity; 2.5.2 Learning-Based Methods; 2.5.2.1 Factorization Methods; 2.5.2.2 Neighborhood-Learning Methods; 2.6 Conclusion; References; 3 Advances in Collaborative Filtering; 3.1 Introduction; 3.2 Preliminaries
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3.2.1 Baseline Predictors3.2.2 The Netflix Data; 3.2.3 Implicit Feedback; 3.3 Matrix Factorization Models; 3.3.1 SVD; 3.3.2 SVD++; 3.3.3 Time-Aware Factor Model; 3.3.3.1 Time Changing Baseline Predictors; 3.3.3.2 Time Changing Factor Model; 3.3.4 Comparison; 3.3.4.1 Predicting Future Days; 3.3.5 Summary; 3.4 Neighborhood Models; 3.4.1 Similarity Measures; 3.4.2 Similarity-Based Interpolation; 3.4.3 Jointly Derived Interpolation Weights; 3.4.3.1 Formal Model; 3.4.3.2 Computational Issues; 3.4.4 Summary; 3.5 Enriching Neighborhood Models; 3.5.1 A Global Neighborhood Model
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3.5.1.1 Building the Model3.5.1.2 Parameter Estimation; 3.5.1.3 Comparison of Accuracy; 3.5.2 A Factorized Neighborhood Model; 3.5.2.1 Factoring Item-Item Relationships; 3.5.2.2 A User-User Model; 3.5.3 Temporal Dynamics at Neighborhood Models; 3.5.4 Summary; 3.6 Between Neighborhood and Factorization; References; 4 Semantics-Aware Content-Based Recommender Systems ; 4.1 Introduction; 4.2 Overview of Content-Based Recommender Systems; 4.2.1 Keyword-Based Vector Space Model; 4.2.2 Methods for Learning User Profiles; 4.2.2.1 Probabilistic Methods; 4.2.2.2 Relevance Feedback
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4.2.2.3 Nearest Neighbors
Weitere Ausg.:
9781489976376
Weitere Ausg.:
9781489976369
Weitere Ausg.:
Erscheint auch als Druck-Ausgabe Recommender Systems Handbook
Sprache:
Englisch
Schlagwort(e):
Electronic books
URL:
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