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  • 标题:Evaluating the Relative Performance of Collaborative Filtering Recommender Systems
  • 本地全文:下载
  • 作者:Humberto Jesús Corona Pampín ; Houssem Jerbi ; Michael P. O'Mahony
  • 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.
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