论文标题
通过等级聚合减轻建议偏见
Alleviating the recommendation bias via rank aggregation
论文作者
论文摘要
推荐系统的主要目标通常被称为“帮助用户查找相关项目”，并且相应地提出了许多建议算法。但是，这些以准确性为导向的方法通常会遇到对流行项目的推荐偏见问题，这不仅不仅欢迎用户，而且还不受项目提供商的欢迎。为了减轻建议偏差问题，我们为现有算法的建议结果提出了一个通用的等级聚合框架，在该算法中，用户和面向项目的排名结果将线性汇总在一起，并控制一个参数，以控制后一种排名过程的权重。在两个现实世界数据集上的典型算法的实验结果表明，该框架有效地提高了任何现有面向准确性的算法的建议公平性，同时避免了明显的准确性损失。
The primary goal of a recommender system is often known as "helping users find relevant items", and a lot of recommendation algorithms are proposed accordingly. However, these accuracy-oriented methods usually suffer the problem of recommendation bias on popular items, which is not welcome to not only users but also item providers. To alleviate the recommendation bias problem, we propose a generic rank aggregation framework for the recommendation results of an existing algorithm, in which the user- and item-oriented ranking results are linearly aggregated together, with a parameter controlling the weight of the latter ranking process. Experiment results of a typical algorithm on two real-world data sets show that, this framework is effective to improve the recommendation fairness of any existing accuracy-oriented algorithms, while avoiding significant accuracy loss.
