Evaluating collaborative filtering recommender systems
Evaluating collaborative filtering recommender systems
复制标题
DOI：
10.1145/963770.963772
复制
发表时间：
2004-01-01
影响因子：
5.6
通讯作者：
Riedl, JT
中科院分区：
文献类型：
--
作者：
Herlocker, JL;Konstan, JA;Riedl, JT
推荐系统已经在许多方面进行了评估，通常是无与伦比的。在这篇文章中，我们回顾了评估协同过滤推荐系统的关键决策：评估的用户任务，使用的分析和数据集的类型，预测质量的测量方法，质量以外的预测属性的评估，以及系统作为一个整体的基于用户的评估。除了回顾以前的研究人员使用的评估策略，我们提出的实证结果，从分析的各种准确性指标的一个内容域，所有的测试指标大致分为三个等价类。每个等价类内的权重都是强相关的，而来自不同等价类的度量是不相关的。
Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.