Recommender systems use information filtering to predict user preferences. They are becoming a vital part of e-business. Recommender Systems: Algorithms and Applications dives into the theoretical underpinnings of these systems and looks at how theory is applied and implemented in actual systems.
Recommender systems use information filtering to predict user preferences. They are becoming a vital part of e-business. Recommender Systems: Algorithms and Applications dives into the theoretical underpinnings of these systems and looks at how theory is applied and implemented in actual systems.
Dr. P. Pavan Kumar received a Ph.D. degree from JNTU, Anantapur, India. He is an Assistant Professor in the Department of Computer Science and Engineering at ICFAI Foundation for Higher Education (IFHE), Hyderabad. His research interests include real-time systems, multi-core systems, high-performance systems, computer vision. Dr. S. Vairachilai earned a Ph.D. degree in Information Technology from Anna University, India. She is an Assistant Professor in the Department of CSE at ICFAI Foundation for Higher Education (IFHE), Hyderabad, Telangana. Prior to this she served in teaching roles an Kalasalingam University and N.P.R College of Engineering and Technology, Tamilnadu, India. Her research interests include Machine Learning, Recommender System and Social Network Analysis. Sirisha Potluri is an Assistant Professor in the Department of Computer Science & Engineering at ICFAI Foundation for Higher Education, Hyderabad. She is pursuing a Ph.D. degree in the area of cloud computing. Her research areas include distributed computing, cloud computing, fog computing, recommender systems and IoT. Dr. Sachi Nandan Mohanty received a Ph.D. degree from IIT Kharagpur, India. He is an Associate Professor in the Department of Computer Science & Engineering at ICFAI Foundation for Higher Education Hyderabad. Prof. Mohanty¿s research areas include data mining, big data analysis, cognitive science, fuzzy decision making, brain-computer interface, and computational intelligence.
Inhaltsangabe
Preface. Acknowledgements. Editors. List of Contributors. Chapter 1 Collaborative Filtering-based Robust Recommender System using Machine Learning Algorithms. Chapter 2 An Experimental Analysis of Community Detection Algorithms on a Temporally Evolving Dataset. Chapter 3 Why This Recommendation: Explainable Product Recommendations with Ontological Knowledge Reasoning. Chapter 4 Model-based Filtering Systems using a Latent-factor Technique. Chapter 5 Recommender Systems for the Social Networking Context for Collaborative Filtering and Content-Based Approaches . Chapter 6 Recommendation System for Risk Assessment in Requirements Engineering of Software with Tropos Goal¿Risk Model. Chapter 7 A Comprehensive Overview to the Recommender System: Approaches, Algorithms and Challenges. Chapter 8 Collaborative Filtering Techniques: Algorithms and Advances. Index.
Preface. Acknowledgements. Editors. List of Contributors. Chapter 1 Collaborative Filtering-based Robust Recommender System using Machine Learning Algorithms. Chapter 2 An Experimental Analysis of Community Detection Algorithms on a Temporally Evolving Dataset. Chapter 3 Why This Recommendation: Explainable Product Recommendations with Ontological Knowledge Reasoning. Chapter 4 Model-based Filtering Systems using a Latent-factor Technique. Chapter 5 Recommender Systems for the Social Networking Context for Collaborative Filtering and Content-Based Approaches . Chapter 6 Recommendation System for Risk Assessment in Requirements Engineering of Software with Tropos Goal¿Risk Model. Chapter 7 A Comprehensive Overview to the Recommender System: Approaches, Algorithms and Challenges. Chapter 8 Collaborative Filtering Techniques: Algorithms and Advances. Index.
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