8 citations · 9 across the 7 of their papers we have counts for
7 papers
Domain-Adaptive Pretraining Enhances Water Treatment Semantic Representation for Large-Scale Structured Literature Mining
Mudi Zhai, Ruihong Qiu, Qingyun Zeng +3
Water treatment research is expanding rapidly, but much of the knowledge acquired from this research remains scattered across unstructured literature. The field still lacks a dedic…
Beyond Static LLM Policies: Imitation-Enhanced Reinforcement Learning for Recommendation
Yi Zhang, Lili Xie, Ruihong Qiu +2
Recommender systems (RecSys) have become critical tools for enhancing user engagement by delivering personalized content across diverse digital platforms. Recent advancements in la…
MARCO: A Cooperative Knowledge Transfer Framework for Personalized Cross-domain Recommendations
Lili Xie, Yi Zhang, Ruihong Qiu +2
Recommender systems frequently encounter data sparsity issues, particularly when addressing cold-start scenarios involving new users or items. Multi-source cross-domain recommendat…
DARLR: Dual-Agent Offline Reinforcement Learning for Recommender Systems with Dynamic Reward
Yi Zhang, Ruihong Qiu, Xuwei Xu +2
Model-based offline reinforcement learning (RL) has emerged as a promising approach for recommender systems, enabling effective policy learning by interacting with frozen world mod…
EMIT- Event-Based Masked Auto Encoding for Irregular Time Series
Hrishikesh Patel, Ruihong Qiu, Adam Irwin +2
Irregular time series, where data points are recorded at uneven intervals, are prevalent in healthcare settings, such as emergency wards where vital signs and laboratory results ar…
Contrastive Learning for Implicit Social Factors in Social Media Popularity Prediction
Zhizhen Zhang, Ruihong Qiu, Xiaohui Xie
On social media sharing platforms, some posts are inherently destined for popularity. Therefore, understanding the reasons behind this phenomenon and predicting popularity before p…
ROLeR: Effective Reward Shaping in Offline Reinforcement Learning for Recommender Systems
Yi Zhang, Ruihong Qiu, Jiajun Liu +1
Offline reinforcement learning (RL) is an effective tool for real-world recommender systems with its capacity to model the dynamic interest of users and its interactive nature. Mos…