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商品描述
This book starts from the classic recommendation algorithms, introduces readers to the basic principles and main concepts of the traditional algorithms, and analyzes their advantages and limitations. Then, it addresses the fundamentals of deep learning, focusing on the deep-learning-based technology used, and analyzes problems arising in the theory and practice of recommender systems, helping readers gain a deeper understanding of the cutting-edge technology used in these systems. Lastly, it shares practical experience with Microsoft 's open source project Microsoft Recommenders. Readers can learn the design principles of recommendation algorithms using the source code provided in this book, allowing them to quickly build accurate and efficient recommender systems from scratch.
商品描述(中文翻譯)
本書從經典的推薦算法開始，向讀者介紹傳統算法的基本原則和主要概念，並分析其優勢和局限性。接著，書中探討深度學習的基本概念，重點介紹所使用的基於深度學習的技術，並分析在推薦系統的理論和實踐中出現的問題，幫助讀者更深入地理解這些系統中使用的前沿技術。最後，本書分享了微軟開源項目 Microsoft Recommenders 的實踐經驗。讀者可以利用本書提供的源代碼學習推薦算法的設計原則，從而快速從零開始構建準確且高效的推薦系統。
作者簡介
Dongsheng Li has been a principal research manager with Microsoft Research Asia (MSRA) since February 2020. His research interests include recommender systems and general machine learning applications. He has published over 100 papers in top-tier conferences and journals and has served as a program committee member for leading conferences.
Dr. Jianxun Lian graduated from the University of Science and Technology of China and is currently a senior researcher with Microsoft Research Asia. His research interests mainly include recommendation systems, user modeling, and deep-learning-related technologies.
Le Zhang is a machine learning architect with Standard Chartered Bank. He has extensive experience in applying cutting-edge machine learning and artificial intelligence technology to accelerate digital transformation for enterprises and start-ups.Kan Ren is a senior researcher with Microsoft Research. His main research interests include spatiotemporal data mining, reasoning, and decision optimization with applications in healthcare, recommender systems, and finance. Kan has published many papers in top-tier conferences on machine learning and data mining.
Tun LU is currently a full professor with the School of Computer Science, Fudan University, China. His research interests include computer-supported cooperative work (CSCW), social computing, recommender systems, and human-computer interaction (HCI). He has published more than 80 peer-reviewed publications in prestigious conferences and journals.
Tao Wu is a Principal Applied Science Manager at Microsoft's Business Applications and Platform Group, and leading product development efforts utilizing large language models and generative AI. He spearheaded the creation of the Microsoft Recommenders project (recently donated to the Linux Foundation), which has become one of the most popular open source projects on recommender systems. Prior to Microsoft, Tao held various research, engineering and leadership positions at Nokia Research Center and MIT CSAIL.
Dr. Xing Xie is currently a senior principal research manager with Microsoft Research Asia. In the past several years, he has published over 300 papers, won the 2022 ACM SIGKDD 2022 Test-of-Time Award and 2021 ACM SIGKDD China Test-of-Time Award, received the 10-Year Impact Award (honorable mention) at ACM SIGSPATIAL 2020, and won the 10-Year Impact Award at ACM SIGSPATIAL 2019. He currently serves on the editorial boards of ACM Transactions on Recommender Systems (ToRS), ACM Transactions on Social Computing (TSC), and ACM Transactions on Intelligent Systems and Technology (TIST).
作者簡介(中文翻譯)
董勝利自2020年2月以來一直擔任微軟亞洲研究院（MSRA）的首席研究經理。他的研究興趣包括推薦系統和一般機器學習應用。他在頂尖會議和期刊上發表了超過100篇論文，並擔任多個領先會議的程序委員會成員。
連建勳博士畢業於中國科學技術大學，目前是微軟亞洲研究院的高級研究員。他的研究興趣主要包括推薦系統、用戶建模和深度學習相關技術。
張樂是一名在渣打銀行工作的機器學習架構師。他在應用尖端機器學習和人工智慧技術以加速企業和初創公司的數位轉型方面擁有豐富的經驗。
任侃是微軟研究院的高級研究員。他的主要研究興趣包括時空數據挖掘、推理和決策優化，應用於醫療保健、推薦系統和金融領域。任侃在機器學習和數據挖掘的頂尖會議上發表了多篇論文。
盧暾目前是中國復旦大學計算機科學學院的全職教授。他的研究興趣包括計算機支持的協作工作（CSCW）、社會計算、推薦系統和人機互動（HCI）。他在著名會議和期刊上發表了超過80篇經過同行評審的出版物。
吳濤是微軟商業應用與平台集團的首席應用科學經理，負責利用大型語言模型和生成式人工智慧領導產品開發工作。他主導了微軟推薦系統項目的創建（最近捐贈給Linux基金會），該項目已成為最受歡迎的開源推薦系統項目之一。在加入微軟之前，吳濤曾在諾基亞研究中心和麻省理工學院CSAIL擔任多個研究、工程和領導職位。
謝星博士目前是微軟亞洲研究院的高級首席研究經理。在過去幾年中，他發表了超過300篇論文，獲得了2022年ACM SIGKDD Test-of-Time獎和2021年ACM SIGKDD中國Test-of-Time獎，並在2020年ACM SIGSPATIAL獲得10年影響獎（榮譽提名），在2019年ACM SIGSPATIAL獲得10年影響獎。他目前擔任ACM推薦系統期刊（ToRS）、ACM社會計算期刊（TSC）和ACM智能系統與技術期刊（TIST）的編輯委員會成員。
