Editors: Abhishek Majumder, Joy Lal Sarkar, Arindam Majumder

Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications

eBook: US $59 Special Offer (PDF + Printed Copy): US $101
Printed Copy: US $71
Library License: US $236
ISBN: 978-981-5136-75-3 (Print)
ISBN: 978-981-5136-74-6 (Online)
Year of Publication: 2023
DOI: 10.2174/97898151367461230101


Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications captures the state of the art in usage of artificial intelligence in different types of recommendation systems and predictive analysis. The book provides guidelines and case studies for application of artificial intelligence in recommendation from expert researchers and practitioners. A detailed analysis of the relevant theoretical and practical aspects, current trends and future directions is presented.

The book highlights many use cases for recommendation systems:

  • - Basic application of machine learning and deep learning in recommendation process and the evaluation metrics
  • - Machine learning techniques for text mining and spam email filtering considering the perspective of Industry 4.0
  • - Tensor factorization in different types of recommendation system
  • - Ranking framework and topic modeling to recommend author specialization based on content.
  • - Movie recommendation systems
  • - Point of interest recommendations
  • - Mobile tourism recommendation systems for visually disabled persons
  • - Automation of fashion retail outlets
  • - Human resource management (employee assessment and interview screening)

This reference is essential reading for students, faculty members, researchers and industry professionals seeking insight into the working and design of recommendation systems.

Audience: Computers science academicians, professionals and students, researchers and engineers working on AI models and recommender systems.


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.

Abhishek Majumder
Tripura University

Joy Lal Sarkar
Tripura University


Arindam Majumder
National Institute of Technology Agartala
Tripura 799046