Recommender System with Machine Learning and Artificial Intelligence

Preface

This book 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 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. In industry point of view, for an individual item or product recommendation system can help to developed for better selling.

Chapter 1 discusses about pros and cons of method like cold-start, scal-ability, sparsity is explained in detail in terms of recommender systems. Various other approaches of recommendation systems are explained like multi-criteria-based recommender systems, risk-aware recommender systems, mobile recommender system, hybrid recommender system, healthcare recommender system, etc.

Chapter 2 provides an insight into the implementation of the recommender system in both tangible and non-tangible products as well as the service care industry.

Chapter 3 discusses both of data exchange and extraction ...