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

Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications
Author :
Publisher : Bentham Science Publishers
Total Pages : 319
Release :
ISBN-10 : 9789815136753
ISBN-13 : 9815136755
Rating : 4/5 (755 Downloads)

Book Synopsis Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications by : Abhishek Majumder

Download or read book Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications written by Abhishek Majumder and published by Bentham Science Publishers. This book was released on 2023-08-16 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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

Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications
Language: en
Pages: 319
Authors: Abhishek Majumder
Categories: Computers
Type: BOOK - Published: 2023-08-16 - Publisher: Bentham Science Publishers

GET EBOOK

Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications captures the state of the art in usage of artif
Artificial Intelligence in Healthcare
Language: en
Pages: 385
Authors: Adam Bohr
Categories: Computers
Type: BOOK - Published: 2020-06-21 - Publisher: Academic Press

GET EBOOK

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of heal
Recommender System with Machine Learning and Artificial Intelligence
Language: en
Pages: 448
Authors: Sachi Nandan Mohanty
Categories: Computers
Type: BOOK - Published: 2020-07-08 - Publisher: John Wiley & Sons

GET EBOOK

This book is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, info
Encyclopedia of Data Science and Machine Learning
Language: en
Pages: 3296
Authors: Wang, John
Categories: Computers
Type: BOOK - Published: 2023-01-20 - Publisher: IGI Global

GET EBOOK

Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big d
Recommender Systems Handbook
Language: en
Pages: 1008
Authors: Francesco Ricci
Categories: Computers
Type: BOOK - Published: 2015-11-17 - Publisher: Springer

GET EBOOK

This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems’ major concepts, theories