3 accepted papers
Causal Inference (CI) plays a crucial role in building unbiased recommender systems. However, most current CI-based debiasing methods only pay attention on either popularity bias or conformity bias. This paper presents a Disentangled Counterfactual Reasoning framework to alleviate dual biases in rec…
In extreme multi-label text classification (XMC), labels usually follow a long-tailed distribution, where most labels only contain a small number of documents and limit the performance of XMC. Data augmentation (DA) is a simple but effective strategy to solve such low-resource problems. In this pape…
Multi-label text classification (MLTC) involves tagging a document with its most relevant subset of labels from a label set. In real applications, labels usually follow a long-tailed distribution, where most labels (called as tail-label) only contain a small number of documents and limit the perform…