Popularity bias has been recently acknowledged as a major bias of critical concern in the field of recommender systems. Although research on popularity bias has gained pace from the last couple of years, this field is believed to be still in its infancy. To advance research in this area, ...
推荐系统漫谈之流行度偏置(popularity bias)与数据链路(Feedback Loop),程序员大本营,技术文章内容聚合第一站。
本文提出的方法可以说是显式地构建了一个所谓的合理的因果模型; 但是如何保证 ^yky^k 就是正确反映了二者匹配程度, 而不夹杂其它的 bias 呢? 概 符号说明 因果模型 主要内容 训练 推断 总结 __EOF__ 分类: Causal Inference , Recommender Systems 标签: 2021 , heuristic , novel , KDD , bias 馒头...
Collaborative filtering algorithms unwittingly produce ranked lists where a few popular items are recommended too frequently while the remaining vast amount of items get not deserved attention, also referred to as the popularity bias problem. Nevertheless, when investigating popularity bias issues in recomm...
Recommender systems are susceptible to popularity bias and can disproportionately recommend popular items. Groups that are underrepresented in the training data may also receive less relevant recommendations from these algorithms compared to others. Ekstrand et al. inves...
Recommender systems for software engineering (RSSEs) assist software engineers in dealing with a growing information overload when discerning alternative development solutions. While RSSEs are becoming more and more effective in suggesting handy recommendations, they tend to suffer from popularity bias, i...
Software of the experiments reported in the SIGIR 2018 paper "Should I Follow the Crowd? A Probabilistic Analysis of the Effectiveness of Popularity in Recommender Systems" evaluationpopularitydatasetrecommender-systembiasprobabilistic-analysis UpdatedSep 14, 2021 ...
†Southern University ofScience and Technologytangb3@sustech.edu.cnABSTRACTGlobal popularity (GP) bias is the phenomenon that popular itemsarerecommendedmuchmorefrequentlythantheyshouldbe,whichgoes against the goal of providing personalized recommendationsand harms user experience and recommendation accuracy. ...
What Drives Readership? An Online Study on User Interface Types and Popularity Bias Mitigation in News Article Recommendations Personalized news recommender systems support readers in finding the right and relevant articles in online news platforms. In this paper, we discuss the introduction of ...
Recommender systems are known to suffer from the popularity bias problem: popular items are recommended frequently, and nonpopular ones rarely, if at all. Prior studies focused on tackling this issue by increasing the number of recommended nonpopular (long-tail) items. However, these...