63 citations · 86 across the 6 of their papers we have counts for
6 papers
Measuring Item Global Residual Value for Fair Recommendation
Jiayin Wang, Weizhi Ma, Chumeng Jiang +4
In the era of information explosion, numerous items emerge every day, especially in feed scenarios. Due to the limited system display slots and user browsing attention, various rec…
Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation
Chongming Gao, Kexin Huang, Jiawei Chen +6
Offline reinforcement learning (RL), a technology that offline learns a policy from logged data without the need to interact with online environments, has become a favorable choice…
Divide and Conquer: Towards Better Embedding-based Retrieval for Recommender Systems From a Multi-task Perspective
Yuan Zhang, Xue Dong, Weijie Ding +3
Embedding-based retrieval (EBR) methods are widely used in modern recommender systems thanks to its simplicity and effectiveness. However, along the journey of deploying and iterat…
Mask the Correct Tokens: An Embarrassingly Simple Approach for Error Correction
Kai Shen, Yichong Leng, Xu Tan +4
Text error correction aims to correct the errors in text sequences such as those typed by humans or generated by speech recognition models. Previous error correction methods usuall…
Differentially Private Optimizers Can Learn Adversarially Robust Models
Yuan Zhang, Zhiqi Bu
Machine learning models have shone in a variety of domains and attracted increasing attention from both the security and the privacy communities. One important yet worrying questio…
Privacy-preserving Network Functionality Outsourcing
Junjie Shi, Yuan Zhang, Sheng Zhong
Since the advent of software defined networks ({SDN}), there have been many attempts to outsource the complex and costly local network functionality, i.e. the middlebox, to the clo…