Information Retrieval and Deep Learning
Description
This book merges the frontiers of artificial intelligence and information retrieval. It navigates the intersection of deep learning technology and information retrieval, offering a structured exploration across various chapters. From foundational concepts to advanced neural network architectures, readers are guided through a journey elucidating the symbiotic relationship between deep learning and information retrieval.
At its core, this work converges computer science, artificial intelligence, and information retrieval, heralding a paradigm shift in knowledge retrieval methodologies. By harnessing deep learning, the book seeks to augment the precision and efficacy of information retrieval systems, thereby enhancing user experience and advancing the frontier of scholarly inquiry. Through rigorous theoretical exposition and empirical validation, the authors substantiate the efficacy of deep learning in real-world applications, offering readers a compendium of knowledge and practical wisdom.
From this book, readers gain insights into the latest research findings and methodologies of deep learning technology in information retrieval. Whether scholars or practitioners, readers are equipped with the tools to navigate the complexities of this interdisciplinary domain. Yet, to fully engage with the material, a foundational understanding of computer science, mathematics, and rudimentary concepts of deep learning and information retrieval is recommended. Through this reading, readers are empowered to navigate the intricacies of information retrieval and deep learning, fostering innovation and advancement in both theory and practice.
The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.
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Persons
Jiafeng Guo is a researcher and doctoral supervisor at the Institute of Computing Technology, Chinese Academy of Sciences, and a professor at University of Chinese Academy of Sciences. He currently serves as the director of the Key Laboratory of Network Data Science and Technology and is the deputy director of the Information Retrieval Special Committee of the Chinese Information Processing Society of China. He is also a recipient of the National Natural Science Foundation for Outstanding Young Scholars. Guo has been engaged in research in the fields of intelligent information retrieval and big data analysis for an extended period. His research achievements have received four Best Paper awards at major international conferences in this field and two national-level scientific and technological awards.
Yanyan Lan is a professor at the Institute for AI Industry Research (AIR) of Tsinghua University. She previously served as a researcher at the Institute of Computing Technology, Chinese Academy of Sciences, and a professor at University of Chinese Academy of Sciences. Her main research areas include information retrieval, machine learning, and AI for science. Lan has received the Best Student Paper Award from SIGIR and a Best Paper Nomination from CIKM. She is also selected as one of the top talents in the National Ten Thousand Talent Program, serving as the secretary-general of the Information Retrieval Special Committee of the Chinese Information Processing Society of China and an associate editor for artificial intelligence.
Xueqi Cheng is the vice director of the Institute of Computing Technology, Chinese Academy of Sciences. He is a recipient of the National Distinguished Young Scientists Fund, a national high-level talent, a Beijing scholar, and a CCF fellow. Cheng holds positions as the secretary-general of the CCF Big Data Expert Committee, secretary-general of the CCF China Digital Economy 50-Person Forum, and vice chairman of the Chinese Information Processing Society of China. His long-term research focuses on network data science, big data systems, social computing, web information retrieval, and data mining. Cheng's research achievements have won six Best Paper awards at top international academic conferences in this field, and his key technologies and system achievements have received national-level scientific and technological awards four times.
Content
Introduction.- Deep Text Indexing.- Deep Text Retrieval.- Deep Text Matching.- Deep Relationship Ranking.- Deep Query Understanding.- Interactive Information Retrieval.- Pre-training Based IR.