26 citations · 32 across the 6 of their papers we have counts for
6 papers
Learning Robust Sequential Recommenders through Confident Soft Labels
Shiguang Wu, Xin Xin, Pengjie Ren +4
Sequential recommenders that are trained on implicit feedback are usually learned as a multi-class classification task through softmax-based loss functions on one-hot class labels.…
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
Qingyao Ai, Ting Bai, Zhao Cao +30
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs. Recently, Large Language Mod…
How Graph Convolutions Amplify Popularity Bias for Recommendation?
Jiajia Chen, Jiancan Wu, Jiawei Chen +3
Graph convolutional networks (GCNs) have become prevalent in recommender system (RS) due to their superiority in modeling collaborative patterns. Although improving the overall acc…
Contrastive State Augmentations for Reinforcement Learning-Based Recommender Systems
Zhaochun Ren, Na Huang, Yidan Wang +7
Learning reinforcement learning (RL)-based recommenders from historical user-item interaction sequences is vital to generate high-reward recommendations and improve long-term cumul…
Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment
Xin Xin, Xiangyuan Liu, Hanbing Wang +8
Recommender systems that learn from implicit feedback often use large volumes of a single type of implicit user feedback, such as clicks, to enhance the prediction of sparse target…
A Self-Correcting Sequential Recommender
Yujie Lin, Chenyang Wang, Zhumin Chen +6
Sequential recommendations aim to capture users' preferences from their historical interactions so as to predict the next item that they will interact with. Sequential recommendati…