Artificial Intelligence and Data Science in Recommendation System : Current Trends Technologies and Applications / edited by Abhishek Majumder, Joy Lal Sarkar and Arindam Majumder.

Format:

Book

Author/Creator:

Majumder, Abhishek, Author.

Contributor:

Majumder, Abhishek, editor.

Sarkar, Joy Lal, editor.

Majumder, Arindam, editor.

Language:

English

Subjects (All):

Artificial intelligence.

Computational intelligence.

Physical Description:

1 online resource (319 pages)

Edition:

First edition.

Place of Publication:

Singapore : Bentham Science Publishers Pte. Ltd., 2023.

Summary:

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.

Contents:

Cover

Title

Copyright

End User License Agreement

Contents

Foreword

Preface

List of Contributors

Study of Machine Learning for Recommendation Systems

Tushar Deshpande1,*, Khushi Chavan1 and Ramchandra Mangrulkar1

INTRODUCTION

Recommendation System

Machine Learning

Supervised learning

Semi-supervised learning

Unsupervised learning

Reinforcement learning

METHODS

Collaborative Filtering

Model-Based

Memory-Based

Content-based Filtering

Hybrid Filtering

Algorithms

Co-clustering

Matrix Factorization

K-Nearest Neighbors

K-means Clustering

Naive Bayes

Random Forest Generated by AI.

Notes:

Includes bibliographical references.

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

Description based on print version record.

ISBN:

9789815136746

9815136747

OCLC:

1399167373