1 citations · 1 across the 11 of their papers we have counts for
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Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation
Tianjun Wei, Huizhong Guo, Yingpeng Du +4
User simulation is increasingly vital to develop and evaluate recommender systems (RSs). While Large Language Models (LLMs) offer promising avenues to simulate user behavior, they…
LLM-Driven Dual-Level Multi-Interest Modeling for Recommendation
Ziyan Wang, Yingpeng Du, Zhu Sun +4
Recently, much effort has been devoted to modeling users' multi-interests based on their behaviors or auxiliary signals. However, existing methods often rely on heuristic assumptio…
Reinforcement Speculative Decoding for Fast Ranking
Yingpeng Du, Tianjun Wei, Zhu Sun +1
Large Language Models (LLMs) have been widely adopted in ranking systems such as information retrieval (IR) systems and recommender systems (RSs). To alleviate the latency of auto-…
Uncertain Multi-Objective Recommendation via Orthogonal Meta-Learning Enhanced Bayesian Optimization
Hongxu Wang, Zhu Sun, Yingpeng Du +3
Recommender systems (RSs) play a crucial role in shaping our digital interactions, influencing how we access and engage with information across various domains. Traditional researc…
Active Large Language Model-based Knowledge Distillation for Session-based Recommendation
Yingpeng Du, Zhu Sun, Ziyan Wang +3
Large language models (LLMs) provide a promising way for accurate session-based recommendation (SBR), but they demand substantial computational time and memory. Knowledge distillat…