摘要翻译：
语义Web的出现要求范式从集中式客户机/服务器体系结构转向分散化和对等计算，使得中心当局的存在变得多余甚至不可能。同时，推荐系统在电子商务中获得了相当大的影响，为人们提供个性化的和符合他们需求的推荐。这些推荐系统传统上是在完全集中的情况下部署的，在远离主机网络外围的封闭社区中运行。我们的目标是在一个基于Agent的框架中结合这两个世界，即分散的对等计算和推荐系统。我们的体系结构以一种流行病风格的协议为特色，以一种健壮的、自组织的方式维护志同道合的对等者的邻居。为了展示我们的体系结构保持可伸缩性、健壮性和向高质量推荐融合的能力，我们在流行的MovieLens数据集上进行离线实验。
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英文标题：
《Emergence of Spontaneous Order Through Neighborhood Formation in
  Peer-to-Peer Recommender Systems》
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作者：
Ernesto Diaz-Aviles, Lars Schmidt-Thieme and Cai-Nicolas Ziegler
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最新提交年份：
2008
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Information Retrieval        信息检索
分类描述：Covers indexing, dictionaries, retrieval, content and analysis. Roughly includes material in ACM Subject Classes H.3.0, H.3.1, H.3.2, H.3.3, and H.3.4.
涵盖索引，字典，检索，内容和分析。大致包括ACM主题课程H.3.0、H.3.1、H.3.2、H.3.3和H.3.4中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Multiagent Systems        多智能体系统
分类描述：Covers multiagent systems, distributed artificial intelligence, intelligent agents, coordinated interactions. and practical applications. Roughly covers ACM Subject Class I.2.11.
涵盖多Agent系统、分布式人工智能、智能Agent、协调交互。和实际应用。大致涵盖ACM科目I.2.11类。
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英文摘要：
  The advent of the Semantic Web necessitates paradigm shifts away from centralized client/server architectures towards decentralization and peer-to-peer computation, making the existence of central authorities superfluous and even impossible. At the same time, recommender systems are gaining considerable impact in e-commerce, providing people with recommendations that are personalized and tailored to their very needs. These recommender systems have traditionally been deployed with stark centralized scenarios in mind, operating in closed communities detached from their host network's outer perimeter. We aim at marrying these two worlds, i.e., decentralized peer-to-peer computing and recommender systems, in one agent-based framework. Our architecture features an epidemic-style protocol maintaining neighborhoods of like-minded peers in a robust, selforganizing fashion. In order to demonstrate our architecture's ability to retain scalability, robustness and to allow for convergence towards high-quality recommendations, we conduct offline experiments on top of the popular MovieLens dataset.
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PDF链接：
https://arxiv.org/pdf/0812.4460