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商品描述
Generative Artificial Intelligence (GenAI) has emerged as a groundbreaking technology that promises to revolutionize many industries as well as people's personal and professional lives. This book discusses GenAI and its role in information access - often referred to as Generative Information Retrieval (GenIR) - or more broadly, information interaction.
The role of GenAI in information access is complex and dynamic, with many dimensions. To address this, following a brief introduction to GenAI and GenIR, the remainder of the book provides eight chapters, each targeting a different dimension or sub-topic. These cover foundations of GenIR, interactions with GenIR systems, adapting them to users, tasks, and scenarios, improving them based on user feedback, GenIR evaluation, the sociotechnical implications of GenAI for information access, recommendations within GenIR, and the future of information access with GenIR.
The book is targeted at graduate students and researchers interested in issues of information retrieval, access, and interactions, as well as applications of GenAI in various informational contexts. While some of the parts assume prior background in IR or AI, most others do not, making this book suitable for adoption in various classes as a primary source or as a supplementary material.
商品描述(中文翻譯)
生成式人工智慧（Generative Artificial Intelligence, GenAI）已成為一項突破性技術，承諾將徹底改變許多行業以及人們的個人和專業生活。本書討論了GenAI及其在資訊存取中的角色——通常稱為生成式資訊檢索（Generative Information Retrieval, GenIR）——或更廣泛地說，資訊互動。
GenAI在資訊存取中的角色是複雜且動態的，具有多個面向。為了應對這一點，在簡要介紹GenAI和GenIR之後，本書的其餘部分提供了八個章節，每個章節針對不同的面向或子主題。這些內容涵蓋了GenIR的基礎、與GenIR系統的互動、根據用戶、任務和情境調整系統、基於用戶反饋改進系統、GenIR評估、GenAI對資訊存取的社會技術影響、GenIR中的推薦，以及未來的資訊存取與GenIR。
本書的目標讀者為對資訊檢索、存取和互動問題感興趣的研究生和研究人員，以及在各種資訊情境中應用GenAI的相關人士。雖然部分內容假設讀者具備資訊檢索（IR）或人工智慧（AI）的背景，但大多數內容並不需要，這使得本書適合在各類課程中作為主要教材或補充材料使用。
作者簡介
Ryen W. White is a Research Scientist, General Manager, and Deputy Lab Director of Microsoft Research in Redmond, WA, USA. He is also an Affiliate Professor at the University of Washington. Ryen's research takes a user- and task-centric view on Artificial Intelligence (AI), with a focus on search and assistance. Technology derived from his and his team's research has significantly improved key business metrics in many Microsoft products, including Bing, Office, and Windows. Ryen is a Fellow of the Association for Computing Machinery (ACM) and of the British Computer Society. He has published over 300 articles and has received over 65 patents on search and related areas, including significant work on mining and modeling search activity at scale. Ryen has received over 20 awards for his technical contributions, including three ACM Special Interest Group on Information Retrieval (SIGIR) Best Paper awards and two SIGIR Test of Time awards. He has received the Karen Spärck Jones Award (2014) and the Tony Kent Strix Award (2022) for outstanding contributions to search. Ryen is one of a handful of scientists who have been inducted into both the SIGIR and the SIGCHI Academies. He currently serves as Editor-in-Chief of ACM Transactions on the Web and as SIGIR Vice Chair.
Chirag Shah is Professor in the Information School (iSchool) at the University of Washington in Seattle, WA, USA. He is also Adjunct Professor with the Paul G. Allen School of Computer Science & Engineering as well as Human Centered Design & Engineering (HCDE). He is the Founding Director for InfoSeeking Lab and Founding Co-Director of Center for Responsibility in AI Systems & Experiences (RAISE). His research focuses on building, auditing, and correcting intelligent information access systems. In addition to creating AI-driven information access systems that provide more personalized reactive and proactive recommendations, he is also focusing on making such systems transparent, fair, and free of biases. Shah is a Distinguished Member of ACM and ASIS&T. He is the recipient of the Karen Spärck Jones Award (2019) and ASIS&T Research in Information Science Award (2024). He has published nearly 200 peer-reviewed articles and authored seven books, including textbooks on data science and machine learning. He also works closely with industrial research labs on cutting-edge problems, typically as a visiting researcher. The most recent engagements include Amazon, Getty Images, Microsoft Research, and Spotify. He currently serves as Editor-in-Chief of Information Matters, published by ASIS&T.
作者簡介(中文翻譯)
**Ryen W. White** 是美國華盛頓州雷德蒙德的微軟研究院的研究科學家、總經理及副實驗室主任。他同時也是華盛頓大學的兼任教授。Ryen 的研究以使用者和任務為中心，專注於人工智慧（AI）的搜尋和輔助技術。他和他的團隊所開發的技術顯著改善了許多微軟產品的關鍵商業指標，包括 Bing、Office 和 Windows。Ryen 是計算機協會（ACM）和英國計算機學會的會士。他已發表超過 300 篇文章，並在搜尋及相關領域獲得超過 65 項專利，包括在大規模搜尋活動挖掘和建模方面的重要工作。Ryen 獲得了超過 20 項技術貢獻獎項，包括三次 ACM 資訊檢索特別興趣小組（SIGIR）最佳論文獎和兩次 SIGIR 時間考驗獎。他獲得了 Karen Spärck Jones 獎（2014 年）和 Tony Kent Strix 獎（2022 年），以表彰他在搜尋領域的傑出貢獻。Ryen 是少數同時被納入 SIGIR 和 SIGCHI 學院的科學家之一。他目前擔任 ACM 網路交易的主編，以及 SIGIR 副主席。
**Chirag Shah** 是美國華盛頓州西雅圖華盛頓大學資訊學院（iSchool）的教授。他同時也是保羅·G·艾倫計算機科學與工程學院及以人為中心的設計與工程（HCDE）的兼任教授。他是 InfoSeeking 實驗室的創始主任，以及人工智慧系統與體驗責任中心（RAISE）的創始共同主任。他的研究專注於構建、審核和修正智能資訊存取系統。除了創建提供更個性化的反應和主動推薦的 AI 驅動資訊存取系統外，他還專注於使這些系統透明、公平且無偏見。Shah 是 ACM 和 ASIS&T 的傑出會員。他獲得了 Karen Spärck Jones 獎（2019 年）和 ASIS&T 資訊科學研究獎（2024 年）。他已發表近 200 篇經過同行評審的文章，並著有七本書籍，包括數據科學和機器學習的教科書。他還與工業研究實驗室密切合作，解決前沿問題，通常以訪問研究員的身份參與。最近的合作包括亞馬遜、蓋蒂圖片社、微軟研究院和 Spotify。他目前擔任 ASIS&T 出版的《資訊事務》的主編。
