Recommender systems
Recommender systems
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DOI：
10.1145/245108.245121
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发表时间：
1997-03-01
影响因子：
22.7
通讯作者：
Varian, HR
中科院分区：
文献类型：
--
作者：
Resnick, P;Varian, HR
推荐系统帮助并增强了这个自然的社会过程。在典型的推荐系统中，人们提供推荐作为输入，然后系统将其汇总并定向到适当的接收者。在某些情况下，主要的转换是聚合;在其他情况下，系统的价值在于它能够在推荐者和寻求推荐者之间进行良好的匹配。第一个推荐系统Tapestry [1]的开发人员创造了“协同过滤”这个短语，其他几个人也采用了它。我们更喜欢更通用的术语“推荐系统”，原因有两个。首先，发送者可能不会明确地与接收者协作，接收者可能彼此不认识。第二，建议可能会建议特别感兴趣的项目，除了指出那些应该被过滤掉。这个特殊的部分包括五个推荐系统的描述。第六篇文章分析了提供建议的激励措施。
Recommender systems assist and augment this natural social process. In a typical recommender system people provide recommendations as inputs, which the system then aggregates and directs to appropriate recipients. In some cases the primary transformation is in the aggregation; in others the system’s value lies in its ability to make good matches between the recommenders and those seeking recommendations.The developers of the first recommender system, Tapestry [1], coined the phrase “collaborative filtering” and several others have adopted it. We prefer the more general term “recommender system” for two reasons. First, recommenders may not explictly collaborate with recipients, who may be unknown to each other. Second, recommendations may suggest particularly interesting items, in addition to indicating those that should be filtered out. This special section includes descriptions of five recommender systems. A sixth article analyzes incentives for provision of recommendations.