利用品类专家提高推荐系统的性能和准确性

Won-Seok Hwang, Ho-Jong Lee, Sang-Wook Kim, Minsoo Lee
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引用次数: 16

摘要

为了满足性能和准确率的要求,提出了多种推荐方法;然而,要同时满足它们是相当困难的,因为它们之间存在权衡。本文引入了类别专家的概念,提出了利用类别专家的评分代替目标用户相似用户的评分进行推荐的方法。我们还扩展了使用目标用户的类别偏好和他/她与类别专家的相似性的方法。我们通过对真实世界数据的大量实验表明,我们的方法在性能和准确性方面明显优于现有方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On using category experts for improving the performance and accuracy in recommender systems
A variety of recommendation methods have been proposed to satisfy the performance and accuracy; however, it is fairly difficult to satisfy both of them because there is a trade-off between them. In this paper, we introduce the notion of category experts and propose the recommendation method by exploiting the ratings of category experts instead of those of the users similar to a target user. We also extend the method that uses both the category preference of a target user and his/her similarity to category experts. We show that our method significantly outperforms the existing methods in terms of performance and accuracy through extensive experiments with real-world data.
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