Runze Wu

Confidence-Aware Matrix Factorization for Recommender Systems

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

Collaborative filtering (CF), particularly matrix factorization (MF) based methods, have been widely used in recommender systems. The literature has reported that matrix factorization methods often produce superior accuracy of rating prediction in recommender systems. However, …

Multi-source Data Multi-task Learning for Profiling Players in Online Games

2020 · 2020 IEEE Conference on Games (CoG)

Profiling game players, especially potential churn and payment prediction, is of paramount importance for online games to improve the product design and the revenue. However, current solutions view either churn or payment prediction as an …

RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender System

2021 · arXiv (Cornell University)

Reinforcement learning based recommender systems (RL-based RS) aim at learning a good policy from a batch of collected data, by casting recommendations to multi-step decision-making tasks. However, current RL-based RS research commonly has a large …

ProMix: Combating Label Noise via Maximizing Clean Sample Utility

2023

Learning with Noisy Labels (LNL) has become an appealing topic, as imperfectly annotated data are relatively cheaper to obtain. Recent state-of-the-art approaches employ specific selection mechanisms to separate clean and noisy samples and then apply …