Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and ApplicationsКНИГИ » ПРОГРАММИНГ
Название: Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications Автор: Abhishek Majumder, Joy Lal Sarkar, Arindam Majumder Издательство: Bentham Books Год: 2023 Страниц: 319 Язык: английский Формат: pdf (true), epub Размер: 45.8 MB
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.
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.
This study provides an overview of recommendation systems and Machine Learning and their types. It briefly outlines the types of Machine Learning, such as supervised, unsupervised, semi-supervised learning and reinforcement. It explores how to implement recommendation systems using three types of filtering techniques: collaborative filtering, content-based filtering, and hybrid filtering. The Machine Learning techniques explained are clustering, co-clustering, and matrix factorization methods, such as Single value decomposition (SVD) and Non-negative matrix factorization (NMF). It also discusses K-nearest neighbors (KNN), K-means clustering, Naive Bayes and Random Forest algorithms. The evaluation of these algorithms is performed on the basis of three metric parameters: F1 measurement, Root mean squared error (RMSE) and Mean absolute error (MAE). For the experimentation, this study uses the BookCrossing dataset and compares analysis based on metric parameters. Finally, it also graphically depicts the metric parameters and shows the best and the worst techniques to incorporate into the recommendation system. This study will assist researchers in understanding the summary of Machine Learning in recommendation systems.
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.
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Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications 
