Crank up the volume: preference bias amplification in collaborative recommendation

VirusCascade：劫持LLM驱动的推荐智能体中的协作反思

VirusCascade: Hijacking Collaborative Reflection in LLM-Powered Recommender Agents

针对LLM驱动推荐智能体的协作反思机制，提出首个黑盒定向推广攻击VirusCascade，利用反思持久性与跨智能体传播实现系统级劫持，在四个数据集上达到最优定向曝光效果。

消除信息传递偏差以缓解基于图神经网络的协同过滤中的流行偏差

Debiasing Message Passing to Mitigate Popularity Bias in GNN-based Collaborative Filtering

本文提出DPAA框架，通过自适应嵌入感知交互加权和层间加权，缓解GNN协同过滤中的流行偏差问题，实验表明其优于现有方法。

Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?

Several studies have identified discrepancies between the popularity of items in user profiles and the corresponding recommendation lists. Such behavior, which concerns a variety of recommendation algorithms, is referred to as popularity bias. Existing work predominantly adopts simple statistical measures, such as the difference of mean or median popularity, to quantify popularity bias. Moreover, it does so irrespective of user characteristics other than the inclination to popular content. In this work, in contrast, we propose to investigate popularity differences (between the user profile and…

Causal Intervention for Leveraging Popularity Bias in Recommendation

Recommender system usually faces popularity bias issues: from the data perspective, items exhibit uneven (long-tail) distribution on the interaction frequency; from the method perspective, collaborative filtering methods are prone to amplify the bias by over-recommending popular items. It is undoubtedly critical to consider popularity bias in recommender systems, and existing work mainly eliminates the bias effect. However, we argue that not all biases in the data are bad -- some items demonstrate higher popularity because of their better intrinsic quality. Blindly pursuing unbiased learning m…

图卷积如何放大推荐中的流行度偏差？

How Graph Convolutions Amplify Popularity Bias for Recommendation?

本文研究图卷积网络如何放大推荐中的流行度偏差，提出一种通用方法，通过聚类估计并移除流行节点在图卷积各层的放大效应，在推理阶段纠正现有模型，有效提升尾部物品推荐且不牺牲流行物品性能。