This book serves as an essential practitioner's guide to the world of recommender algorithms as it stands in early 2026. We begin with the indispensable baselines-from classic neighborhood models to powerful matrix factorization-and build toward the sophisticated deep learning architectures that power today's largest platforms, including hybrids for CTR prediction and state-of-the-art sequential models.
A core theme of this guide is the practical integration of the latest technological breakthroughs. We dedicate significant attention to the transformative impact of Large Language Models (LLMs), offering architectural blueprints for leveraging them as powerful semantic feature extractors, building reliable Retrieval-Augmented Generation (RAG) pipelines, and designing the next wave of generative and conversational recommender agents. Furthermore, we explore the critical role of multimodal models like CLIP for solving visual cold-start problems and provide insights into specialized areas like debiasing and fairness. This is more than a survey; it is a toolkit for the modern engineer. Each section balances conceptual depth with pragmatic advice on implementation, scalability, and production readiness, making it the definitive resource for professionals tasked with creating value through personalization.外文書商品之書封，為出版社提供之樣本。實際出貨商品，以出版社所提供之現有版本為主。部份書籍，因出版社供應狀況特殊，匯率將依實際狀況做調整。
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