GroupLens: Scalable Collaborative Filtering for the Internet

Grouplens的研究项目探讨了协同过滤方法，以解决互联网信息系统用户的信息过载问题。 协同过滤是一种技术，它使用文档的一些读者的意见来预测其他潜在读者对该文档的兴趣。 这个项目 建立在成功的Groupens协同过滤系统的Usenet新闻，探讨更详细的人为因素和算法问题的协同过滤。 这 项目 地址 三 基本 问题 在协同过滤中：如何以最小的努力有效地测量用户的兴趣，如何使用这些兴趣测量 准确预测其他 用户的兴趣， 和 如何 到 整合 是非协作 和协同过滤方法来创建鲁棒的过滤环境。 的 研究方法 依赖 后 三种实验方法。 一组受控的实验测试被用来收集完整的评级数据集，以直接衡量协同过滤算法的有效性。 采用小规模公开试验的方式收集GroupLens系统实际使用情况的数据。 调查研究是用来获得用户的意见和反馈，从两个试验。

The Grouplens research project explores collaborative filtering approaches to address information overload for users of Internet information systems. Collaborative filtering is a technology that uses the opinions of some readers of a document to predict the interest of other potential readers in that document. This project builds upon the successful Grouplens collaborative filtering system for Usenet news to explore in more detail the human factors and algorithmic issues of collaborative filtering. This project addresses three fundamental problems in collaborative filtering: how to effectively measure user interest with minimal effort from users, how to use these measures of interest to produce accurate predictions of other user's interest, and how to integrate non-collaborative and collaborative filtering approaches to create robust filtering environments. The research approach relies upon three experimental methods. A set of controlled experimental trials is used to gather complete ratings datasets to directly measure the effectiveness of collaborative filtering algorithms. A small- scale public trial is used to collect data on the actual usage of the GroupLens system. Survey research is used to obtain user opinions and feedback from both trials.