CAREER: Algorithmic Issues in Collaborative Filtering

信息时代的承诺是提供比以往任何时候都多的信息。 不幸的是，人类管理和处理信息的能力基本上保持不变。这种不匹配的结果是信息过载。 本计画延伸一种减轻资讯超载的方法--协同过滤--以提供一种新的方式来协助个人进行个人资讯的选择。 协同过滤使用社区成员关于信息对象的意见来向社区中的每个个体推荐这些对象的子集。 该技术适用于每个用户都评估了许多信息对象的小型社区。 该项目使用复杂的统计和计算技术，将协同过滤扩展到拥有数百万用户的社区，这些用户可能只评估了可用项目的一小部分。 作为这项工作的一部分，研究人员正在开发信心的衡量标准，以帮助用户确定对特定推荐的信任程度。 该研究项目将使已经蓬勃发展的个性化行业从利基市场进入主流社区和市场;同时，它将为其他研究人员提供评估新过滤算法和技术所需的措施。 http://www.cs.umn.edu/Research/GroupLens

The promise of the information age is the delivery of more information than has ever been available before. Unfortunately, human ability to manage and process information remains mostly unchanged. The result of this mismatch is information overload. This project extends one approach to alleviating information overload--collaborative filtering--to provide a new way to assist individuals in personal information selection. Collaborative filtering uses the opinions of member of a community about information objects to recommend a subset of those objects to each individual in the community. The technique works well for small communities in which each user has evaluated many of the information objects. This project uses sophisticated statistical and computation techniques to extend collaborative filtering to communities with millions of users who may have evaluated only a fraction of a percent of the available items. As part of this work, the researchers are developing measures of confidence to help users determine how much faith to place in a particular recommendation. This research project will allow an already booming personalization industry to move from niche markets into mainstream communities and markets; at the same time, it will provide the measures needed for other researchers to evaluate new filtering algorithms and techniques. http://www.cs.umn.edu/Research/GroupLens