82 citations · 101 across the 10 of their papers we have counts for
10 papers
Generative Auto-Bidding with Unified Modeling and Exploration
Mingming Zhang, Feiqing Zhuang, Na Li +7
Automated bidding is central to modern digital advertising. Early rule-based methods lacked adaptability, while subsequent Reinforcement Learning approaches modeled bidding as a Ma…
Q-Regularized Generative Auto-Bidding: From Suboptimal Trajectories to Optimal Policies
Mingming Zhang, Na Li, Zhuang Feiqing +8
With the rapid development of e-commerce, auto-bidding has become a key asset in optimizing advertising performance under diverse advertiser environments. The current approaches fo…
LFD: Layer Fused Decoding to Exploit External Knowledge in Retrieval-Augmented Generation
Yang Sun, Zhiyong Xie, Lixin Zou +7
Retrieval-augmented generation (RAG) incorporates external knowledge into large language models (LLMs), improving their adaptability to downstream tasks and enabling information up…
Multi-Interest Recommendation: A Survey
Zihao Li, Qiang Chen, Lixin Zou +2
Existing recommendation methods often struggle to model users' multifaceted preferences due to the diversity and volatility of user behavior, as well as the inherent uncertainty an…
Efficient and Robust Regularized Federated Recommendation
Langming Liu, Wanyu Wang, Xiangyu Zhao +9
Recommender systems play a pivotal role across practical scenarios, showcasing remarkable capabilities in user preference modeling. However, the centralized learning paradigm predo…
Efficient Sparse Attention needs Adaptive Token Release
Chaoran Zhang, Lixin Zou, Dan Luo +4
In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide array of text-centric tasks. However, their `large' scale introduces significa…
Evolutionary Reinforcement Learning: A Systematic Review and Future Directions
Yuanguo Lin, Fan Lin, Guorong Cai +3
In response to the limitations of reinforcement learning and evolutionary algorithms (EAs) in complex problem-solving, Evolutionary Reinforcement Learning (EvoRL) has emerged as a…
One Model for All: Large Language Models are Domain-Agnostic Recommendation Systems
Zuoli Tang, Zhaoxin Huan, Zihao Li +6
Sequential recommendation systems aim to predict users' next likely interaction based on their history. However, these systems face data sparsity and cold-start problems. Utilizing…
Generative Meta-Learning for Zero-Shot Relation Triplet Extraction
Wanli Li, Tieyun Qian, Yi Song +4
Zero-shot Relation Triplet Extraction (ZeroRTE) aims to extract relation triplets from texts containing unseen relation types. This capability benefits various downstream informati…
A Survey on Reinforcement Learning for Recommender Systems
Yuanguo Lin, Yong Liu, Fan Lin +5
Recommender systems have been widely applied in different real-life scenarios to help us find useful information. In particular, Reinforcement Learning (RL) based recommender syste…