Machine Learning-Based Personalized Recommendation Algorithms and Their Applications
CRC Press
1st Edition
Will be published approx. on 26. November 2026
Book
Hardback
168 pages
978-1-041-28567-0 (ISBN)
Description
This book introduces innovative machine learning-based algorithms and a prototype system for personalized book recommendations, addressing key challenges such as inefficiency, data sparsity, cold-start issues, and user interest drift.
It begins with an overview of machine learning and recommender system theories, followed by the presentation of three algorithms: a frequent itemset mining approach using three-dimensional matrices and vectors; a collaborative filtering method incorporating penalty factors and temporal weights; and a hybrid collaborative filtering technique combining user attributes with item ratings. Each algorithm is thoroughly explained, including its design principles, mathematical models, and experimental results. Tests on public datasets highlight their effectiveness in improving recommendation accuracy, recall, and coverage, while offering robust solutions to persistent challenges in the field.
This work is a valuable resource for researchers, students, engineers, and practitioners in machine learning and recommender systems, as well as professionals seeking to implement advanced recommendation solutions in practical applications.
It begins with an overview of machine learning and recommender system theories, followed by the presentation of three algorithms: a frequent itemset mining approach using three-dimensional matrices and vectors; a collaborative filtering method incorporating penalty factors and temporal weights; and a hybrid collaborative filtering technique combining user attributes with item ratings. Each algorithm is thoroughly explained, including its design principles, mathematical models, and experimental results. Tests on public datasets highlight their effectiveness in improving recommendation accuracy, recall, and coverage, while offering robust solutions to persistent challenges in the field.
This work is a valuable resource for researchers, students, engineers, and practitioners in machine learning and recommender systems, as well as professionals seeking to implement advanced recommendation solutions in practical applications.
More details
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Academic, Postgraduate, Professional Practice & Development, Professional Reference, Professional Training, Undergraduate Advanced, and Undergraduate Core
Illustrations
26 s/w Tabellen, 59 s/w Abbildungen, 5 s/w Photographien bzw. Rasterbilder, 54 s/w Zeichnungen
26 Tables, black and white; 54 Line drawings, black and white; 5 Halftones, black and white; 59 Illustrations, black and white
Dimensions
Height: 254 mm
Width: 178 mm
ISBN-13
978-1-041-28567-0 (9781041285670)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Persons
Chaohui Liu is a Senior Experimentalist at Zhengzhou University of Aeronautics, China. His research interests focus on artificial intelligence and machine learning.
Lingling Li is Professor, PhD supervisor, and Vice President at Zhengzhou University of Aeronautics, China. Her research focuses on computer vision.
Lingling Li is Professor, PhD supervisor, and Vice President at Zhengzhou University of Aeronautics, China. Her research focuses on computer vision.
Content
1. Introduction 2. Theoretical Foundations of Machine Learning 3. Theoretical Foundations of Personalized Recommendation Algorithms 4. A Frequent Itemset Mining Algorithm Using a Novel Three-Dimensional Itemset Matrix and Vectors 5. Collaborative Filtering Algorithm Integrating Penalty Factors and Temporal Weighting 6. Collaborative Filtering Algorithm Based on User Attributes and Item Ratings 7. Prototype System for Personalized Book Recommendation 8. Conclusions and Future Work