40 citations · 82 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 13 cited
Understanding Contrastive Learning via Distributionally Robust Optimization
Junkang Wu, Jiawei Chen, Jiancan Wu +3
This study reveals the inherent tolerance of contrastive learning (CL) towards sampling bias, wherein negative samples may encompass similar semantics (\eg labels). However, existi…
cs.IR2023★ 40 cited
On the Theories Behind Hard Negative Sampling for Recommendation
Wentao Shi, Jiawei Chen, Fuli Feng +4
Negative sampling has been heavily used to train recommender models on large-scale data, wherein sampling hard examples usually not only accelerates the convergence but also improv…
cs.IR2023★ 29 cited
Adap-: Adaptively Modulating Embedding Magnitude for Recommendation
Jiawei Chen, Junkang Wu, Jiancan Wu +3
Recent years have witnessed the great successes of embedding-based methods in recommender systems. Despite their decent performance, we argue one potential limitation of these meth…