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This book explores the advanced recommendation algorithms employed by leading internet companies in China, delving into their ideological underpinnings and technical frameworks.

Organised into ten chapters, the book provides a comprehensive overview of recommendation systems, including foundational concepts, feature engineering, embedding techniques, and the algorithms driving key components such as recall, rough ranking, fine ranking, and re-ranking. It also tackles practical challenges in algorithm implementation, such as multi-task and multi-scenario recommendations, cold start issues for new users and content, model effectiveness evaluation, and strategies for identifying and resolving problems. The concluding chapter offers practical insights into work methodologies, learning approaches, and interview preparation tailored for recommendation algorithm engineers.

It serves as a valuable resource for professionals in recommendation systems, computational advertising, and personalized search, as well as students pursuing interests in recommendation algorithms, machine learning, and artificial intelligence-especially those aspiring to careers in these domains.