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 $94
Printed Copy: US $65
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.


I have the pleasant task of writing the foreword for the book Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies, and Applications. This is a work edited by Abhishek Majumder, Joy Lal Sarkar of Tripura University, India, and Arindam Majumder of NIT Agartala, India. This book spans certain very crucial and current issues on the theory and application of Artificial Intelligence and Machine Learning. One of the most widely used applications is recommendation systems, which millions of people use on an everyday basis for shopping and entertainment.

The methods in AI and NLP have been in development for several decades. Classification methods and neural networks have also existed for a long time. However, the advent of large-scale gathering of social and user data has recently allowed theoretical techniques to be tested and proved in everyday practice. As a student of AI in the late 80s at IISc, it was difficult for me to imagine this day. We have seen the progression of the methods of pattern recognition and statistical classification methods. There was an interesting twist in the developments of AI systems where in the late 60s, it appeared that linear classification systems and Perceptron training algorithms would progress far. But the failure to solve XOR logic problems led the researchers to believe that these would be ineffective. This has now been very much established to be a fallacy. But the twist took AI research into the development of logic and systems called expert systems. It was imagined that these expert systems would have the real world and the real world experts' knowledge. The knowledge acquisition bottleneck and lack of trainability of the expert systems were their downfalls. There is now a resurgence of another type of system that is filling in this role: the recommender system. These systems are bringing together diverse methods and techniques in AI, Data Science, and large data sets into human interfaces.

Thus, it gives me immense pleasure to see that this compilation has various applications, such as industry 4.0. Going further, we have applications presented here on deep learning, developing applications, movie recommendations, and movie reviews. One of the major applications these days is through natural language processing methods to perform sentiment analysis with data from social media. This is applied to movie reviews for tourist reviews, assessment of prediction and fashion retail, and exploring human resource recruitment and selection aspects. In addition to the very current topics that have been compiled, it is seen that there is a good diversity in the contributors to this volume.

I wish this compilation the best wishes and that the readers might benefit most from it.

Atul Negi
School of Computer and Information Sciences
University of Hyderabad