文章基本信息

标题：Evaluating the Relative Performance of Collaborative Filtering Recommender Systems
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作者：Humberto Jesús Corona Pampín ; Houssem Jerbi ; Michael P. O'Mahony 等
期刊名称：Journal of Universal Computer Science
印刷版ISSN：0948-6968
出版年度：2015
卷号：21
期号：13
页码：1849-1868
出版社：Graz University of Technology and Know-Center
摘要：Past work on the evaluation of recommender systems indicates that collaborative filtering algorithms are accurate and suitable for the top-N recommendation task. Further, the importance of performance beyond accuracy has been recognised in the literature. Here, we present an evaluation framework based on a set of accuracy and beyond accuracy metrics, including a novel metric that captures the uniqueness of a recommendation list. We perform an in-depth evaluation of three well-known collaborative filtering algorithms using three datasets. The results show that the user-based and item-based collaborative filtering algorithms have a high inverse correlation between popularity and diversity and recommend a common set of items at large neighbourhood sizes. The study also finds that the matrix factorisation approach leads to more accurate and diverse recommendations, while being less biased toward popularity.