Popularity Bias in Ranking and Recommendationdoi:10.1145/3306618.3314309Himan AbdollahpouriACMNational Conference on Artificial Intelligence
Ranking Adjustment:在推荐系统重排阶段boost不流行商品。方法缺乏理论支持。 本文反其道行之,提出在训练阶段有效利用商品流行度偏差。此外,一些平台需要在系统中引入期望的流行度偏差,例如,boost 未来可能流行的商品。本研究旨在填补有效利用流行度偏差来提高推荐准确率的研究空白。 本文在流行度偏差上的因果假设如下图所...
可以看到,这里首先拟合了历史数据,然后使用user-item matching组件ELU来做deconfounded ranking。作者提出这种方法为Popularity-bias Deconfounding (PD). 基于推理的popularity bias调整 这一步的目的是得到有利的bias,记为 \bar z 。那么我们侵入式推理的公式就是: 这里\bar m 代表当前bias的popularity value,通过一...
Such inherent prejudice of recommender systems is also referred to as the popularity bias problem and leads to ”the rich get richer” impact in favor of a few popular items (Kamishima et al., 2014). In recent years, scrutinizing the issues of popularity bias caused by recommendation ...
本文用True positive rate(TPR)和audience size之间的基尼系数来衡量推荐系统的popularity bias,其中TPRi=Cti/AiTPRi=Cit/Ai,CtiCit表示物品i在t时刻被点击的次数,AiAi表示物品i的潜在用户群体规模。这个TPR可以理解为,喜欢每个物品的用户,被推荐到这个物品的几率是否是一致的。从后文中Figure 4可以看出,喜欢流行物品...
Popularity bias q1q1 和rr 具有高相似度 相似度随着维度降低而增加 相似度随着训练的变化 ReSN: Regulartion with Spectral Norm Lin S., Gao C., Chen J., Zhou S., Hu B., Feng Y., Chen C. and Wang C. How do recommendation models amplify popularity bias? An analysis from the spectral per...
A Powerful Tool for Live Crowd and Traffic Data. geocodingmapsgoogle-mapspopularityosm-datalivedata UpdatedDec 22, 2023 CSS rahmanidashti/FairBook Star4 FairBook: A Reproducibility Study on The Unfairness of Popularity Bias in Book Recommendation (Bias@ECIR 2022) ...
This is an implemention for our SIGIR 2021 paper "Causal Intervention for Leveraging Popularity Bias in Recommendation" based on tensorflow. This work was completed when Yang Zhang was an intern atWeChat, Tencent. Requirements tensorflow == 1.14 ...
I decided to scrape the most popular lists to avoid selection bias. I used the Scrapy framework in Python to scrape about 22,000 unique books in my data set.
Motivated by causal effects, we propose a novel counterfactual inference framework named Mitigating Popularity Bias in Recommendation via Counterfactual Inference (MPCI), which enables us to capture the popularity bias as the direct causal effect of the prediction score, and we eliminate popularity bias...