通过排序融合技术减轻推荐偏见(cs.SI)---用户7199428

推荐系统最基本的目标被认为是“帮助用户找到相关品项”，并且大量的推荐算法也都是依次提出的。但是，这些基于准确率的方法经常面临着偏重于潮流商品的问题。这个问题不仅招致用户的不满，而且也无法让商品供应者满意。为了缓解推荐偏见问题，我们提出了一个通用的排序聚合框架可以用于整合现有算法的推荐结果。该框架将基于用户和基于商品的排序结果线性聚合到一起，并且有一个参数负责控制后续排序过程的权重。基于两个真实数据的数据集并采用一个典型算法的实验结果，体现出这个框架能有效提高任何现有的基于准确性算法的推荐公平性，并且规避了显著地精度损失。

原文题目：Alleviating the recommendation bias via rank aggregation

原文：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.

原文作者：Qiang Dong, Quan Yuan, Yang-Bo Shi

原文地址：https://arxiv.org/abs/2004.10393