Publications (9)
LearnAlign: Data Selection for LLM Reinforcement Learning with Improved Gradient Alignment
Shipeng Li, Zhiqin Yang, Shikun Li +7
Reinforcement learning with verifiable rewards (RLVR) has become a key technique for enhancing LLMs' reasoning abilities, yet its data inefficiency remains a major bottleneck. To a…
Online Item Cold-Start Recommendation with Popularity-Aware Meta-Learning
Yunze Luo, Yuezihan Jiang, Yinjie Jiang +5
With the rise of e-commerce and short videos, online recommender systems that can capture users' interests and update new items in real-time play an increasingly important role. In…
Representation Quantization for Collaborative Filtering Augmentation
Yunze Luo, Yinjie Jiang, Gaode Chen +9
As the core algorithm in recommendation systems, collaborative filtering (CF) algorithms inevitably face the problem of data sparsity. Since CF captures similar users and items for…
Denoising Implicit Feedback for Cold-start Recommendation
Gaode Chen, Shicheng Wang, Shikun Li +8
Implicit feedback is widely used in recommender systems due to its accessibility and generality, yet it usually presents noisy samples (e.g., clickbait, position bias). Meanwhile,…
A Unified Framework for Cross-Domain Recommendation
Jiangxia Cao, Shen Wang, Gaode Chen +4
In addressing the persistent challenges of data-sparsity and cold-start issues in domain-expert recommender systems, Cross-Domain Recommendation (CDR) emerges as a promising method…
Prompt Tuning for Item Cold-start Recommendation
Yuezihan Jiang, Gaode Chen, Wenhan Zhang +6
The item cold-start problem is crucial for online recommender systems, as the success of the cold-start phase determines whether items can transition into popular ones. Prompt lear…
Towards Comprehensible Recommendation with Large Language Model Fine-tuning
Yunze Luo, Yinjie Jiang, Gaode Chen +4
Recommender systems have become increasingly ubiquitous in daily life. While traditional recommendation approaches primarily rely on ID-based representations or item-side content f…
Exploring Periodicity and Interactivity in Multi-Interest Framework for Sequential Recommendation
Gaode Chen, Xinghua Zhang, Yanyan Zhao +2
Sequential recommendation systems alleviate the problem of information overload, and have attracted increasing attention in the literature. Most prior works usually obtain an overa…
A Cross-City Federated Transfer Learning Framework: A Case Study on Urban Region Profiling
Gaode Chen, Yijun Su, Xinghua Zhang +6
Data insufficiency problems (i.e., data missing and label scarcity) caused by inadequate services and infrastructures or imbalanced development levels of cities have seriously affe…