Preface

A recommendation System is an intelligent computer-based system that serves as a guide and suggests, as per the preferences of the person. It uses state-of-the-art technologies like Big Data, Machine Learning, Artificial Intelligence, etc., and benefits both the consumer and the merchant. Recommendation System is becoming very popular as it serves as a guide for the activity that a person or a group plans to perform in the best possible manner, given the constraints imposed by the user(s). Software tools and techniques provide advice on items to be used by a user. The recommendations are to inspire its users to buy different products. This music creation initiative includes specialists in several fields, including Artificial Intelligence, Human-Computer Interaction, Data Mining, Analytics, Adaptive User Interfaces, and Decision Support Systems, etc. In this book, the major concepts of recommender systems, theories, methodologies, challenges and advanced applications of recommenders systems are imposed on this diversity. This book comprises various parts: techniques, applications and assessments of recommendation systems, interactions with these systems, and advanced algorithms. The topic of recommendation systems is highly diverse, since it makes it possible for users to make recommendations using different types of user preferences and user needs data. Collaborative filtering processes, content-based methods, and knowledge-based methods are the most common methods in recommending systems. Such three approaches are the basic foundations of recommendation systems. Specialized methods for different data fields and contexts, such as time, place, and social information, have been developed in recent years. Many developments for specific scenarios have been suggested, and techniques have been adapted to different fields of use.