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.