5 papers
Unbiased Recommender Systems with Implicit Feedback
Md Aminul Islam
Recommender systems typically rely on implicit feedback (e.g., clicks) to infer user preferences. However, such data is inherently prone to various biases, including position bias…
Debiasing Message Passing to Mitigate Popularity Bias in GNN-based Collaborative Filtering
Md Aminul Islam, Ahmed Sayeed Faruk, Sourav Medya +1
Collaborative filtering (CF) models based on graph neural networks (GNNs) achieve strong performance in recommender systems by propagating user-item signals over interaction graphs…
Post-hoc Popularity Bias Correction in GNN-based Collaborative Filtering
Md Aminul Islam, Elena Zheleva, Ren Wang
User historical interaction data is the primary signal for learning user preferences in collaborative filtering (CF). However, the training data often exhibits a long-tailed distri…
A Control Function Framework for Mitigating Position Bias in Learning to Rank Systems
Md Aminul Islam, Kathryn Vasilaky, Elena Zheleva
Learning-to-rank (LTR) systems commonly depend on implicit feedback, such as user clicks, because it is easy to collect and can serve as a valuable signal of user preferences. Howe…
Prompt-Based LLMs for Position Bias-Aware Reranking in Personalized Recommendations
Md Aminul Islam, Ahmed Sayeed Faruk
Recommender systems are essential for delivering personalized content across digital platforms by modeling user preferences and behaviors. Recently, large language models (LLMs) ha…