Description
Deep Learning Recommender Systems provides a thorough examination of how deep learning technologies revolutionize recommendation engines across industries. The book addresses fundamental concepts in neural networks, collaborative filtering, and content-based approaches while diving into advanced architectures like autoencoders, RNNs, and attention mechanisms.
Authored by Zhe Wang and Chao Pu, this resource bridges theory and practice by presenting real-world applications in e-commerce, streaming services, and social media platforms. Readers will learn to implement scalable recommendation systems, optimize model performance, and handle challenges like data sparsity and cold-start problems. The book combines mathematical foundations with practical code examples, making it invaluable for machine learning engineers, data scientists, and researchers developing next-generation recommendation systems.
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