Recommender System with Machine Learning and Artificial Intelligence: Practical Tools and Applications in Medical, Agricultural and Other Industries - Hardcover

Über die Autorin bzw. den Autor

Sachi Nandan Mohanty received his PhD from IIT Kharagpur, India in 2015 and is now at ICFAI Foundation for Higher Education, Hyderabad, India.

Jyotir Moy Chatterjee is working as an Assistant Professor (IT) at Lord Buddha Education Foundation, Kathmandu, Nepal. He has completed M.Tech in Computer Science & Engineering from Kalinga Institute of Industrial Technology, Bhubaneswar, India.

Sarika Jain obtained her PhD in the field of Knowledge Representation in Artificial Intelligence in 2011. She has served in the field of education for over 18 years and is currently in service at the National Institute of Technology, Kurukshetra.

Ahmed A. Elngar is the Founder and Head of Scientific Innovation Research Group (SIRG) and Assistant Professor of Computer Science at the Faculty of Computers and Information, Beni-Suef University, Egypt.

Priya Gupta is working as an Assistant Professor in the Department of Computer Science at Maharaja Agrasen College, University of Delhi. Her Doctoral Degree is from BIT (Mesra), Ranchi.

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This book comprehensively covers all the topics of cutting-edge and emerging recommender systems that provide personalized recommendations of items or services based on past behavior.

This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. Recommender system methods have been adapted to diverse applications including social networking, movie recommendation, query log mining, news recommendations, and computational advertising.

The 21 chapters are placed in 5 broad sections looking at Introduction to Recommender Systems; Machine Learning-Based Recommender Systems; Content-Based Recommender Systems; Blockchain & IoT-Based Recommender Systems; and Healthcare Recommender Systems. Recommendations in specific 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. Various robustness aspects of recommender systems, such as trust-based recommendation system, recommendation system on tourist, medical sciences, and the agricultural field are discussed. In addition, current topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria decision support systems, and active learning systems, are introduced together with applications.

Audience
The book will be used by engineers in information technology, artificial intelligence, human-computer interaction, machine learning and analytics specialists, as well as marketeers and web managers in industry.