《推荐系统手册(第二版)》权威指南:Ricci、Rokach与Shapira编著

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《推荐系统手册第二版》是由弗朗西斯科·里奇(Francesco Ricci)、利奥尔·罗卡奇(Lior Rokach)和布拉查·夏皮拉(Bracha Shapira)三位编辑共同编撰的一本权威著作。该书是推荐系统领域的重要参考资料,第二版在2011年和2015年相继发行,涵盖了丰富的理论与实践内容。推荐系统是一种广泛应用的技术,旨在根据用户的历史行为、偏好和环境因素,为他们提供个性化的产品或服务建议。 书中详细介绍了推荐算法的各种类型,包括基于内容的推荐(Content-Based Filtering)、协同过滤(Collaborative Filtering)、矩阵分解(Matrix Factorization)、深度学习方法以及混合推荐策略等。这些方法针对不同的数据结构和用户群体进行优化,以提高推荐的准确性和用户满意度。此外,作者还探讨了推荐系统的评估指标,如精确度、召回率、覆盖率和多样性,以及如何处理冷启动问题和处理用户隐私保护等问题。 编辑们是计算机科学和信息系统的专家,分别来自博尔扎诺自由大学(Free University of Bozen-Bolzano,意大利)和本古里安大学(Ben-Gurion University of the Negev,以色列),他们的深厚学术背景使得本书内容既有理论深度,又贴近实际应用。《推荐系统手册第二版》不仅适合研究人员、专业人士,也对那些希望深入了解推荐系统原理和技术的学生和工程师具有重要的参考价值。 本书还包括了版权信息,如国际标准书号(ISBN)和电子书版本的DOI,以及图书馆编目号,表明了其在学术界的正式地位。此外,它强调所有版权权益归Springer Science+Business Media所有,禁止未经许可的复制、重印、传播或任何形式的信息存储与检索。 《推荐系统手册第二版》是一本全面而深入的指南,为读者揭示了推荐系统设计、实施和优化的核心知识,以及在这个快速发展的技术领域中的最新进展。通过阅读这本书,读者可以掌握构建高效、用户友好的个性化推荐系统的关键要素,并在实际项目中加以应用。
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The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the last decade, many of them have also been successfully deployed in commercial environments. Recommender Systems Handbook, an edited volume, is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. Theoreticians and practitioners from these fields continually seek techniques for more efficient, cost-effective and accurate recommender systems. This handbook aims to impose a degree of order on this diversity, by presenting a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, challenges and applications. Extensive artificial applications, a variety of real-world applications, and detailed case studies are included. Recommender Systems Handbook illustrates how this technology can support the user in decision-making, planning and purchasing processes. It works for well known corporations such as Amazon, Google, Microsoft and AT&T. This handbook is suitable for researchers and advanced-level students in computer science as a reference.