32 citations · 65 across the 25 of their papers we have counts for
7 papers · 1 filter
RecoWorld: Building Simulated Environments for Agentic Recommender Systems
Fei Liu, Xinyu Lin, Hanchao Yu +12
We present RecoWorld, a blueprint for building simulated environments tailored to agentic recommender systems. Such environments give agents a proper training space where they can…
Navigating Through Paper Flood: Advancing LLM-based Paper Evaluation through Domain-Aware Retrieval and Latent Reasoning
Wuqiang Zheng, Yiyan Xu, Xinyu Lin +3
With the rapid and continuous increase in academic publications, identifying high-quality research has become an increasingly pressing challenge. While recent methods leveraging La…
Heterogeneous User Modeling for LLM-based Recommendation
Honghui Bao, Wenjie Wang, Xinyu Lin +4
Leveraging Large Language Models (LLMs) for recommendation has demonstrated notable success in various domains, showcasing their potential for open-domain recommendation. A key cha…
EARN: Efficient Inference Acceleration for LLM-based Generative Recommendation by Register Tokens
Chaoqun Yang, Xinyu Lin, Wenjie Wang +4
Large Language Model-based generative recommendation (LLMRec) has achieved notable success, but it suffers from high inference latency due to massive computational overhead and mem…
Rec: Towards Large Recommender Models with Reasoning
Runyang You, Yongqi Li, Xinyu Lin +4
Large recommender models have extended LLMs as powerful recommenders via encoding or item generation, and recent breakthroughs in LLM reasoning synchronously motivate the explorati…
Collaboration of Large Language Models and Small Recommendation Models for Device-Cloud Recommendation
Zheqi Lv, Tianyu Zhan, Wenjie Wang +6
Large Language Models (LLMs) for Recommendation (LLM4Rec) is a promising research direction that has demonstrated exceptional performance in this field. However, its inability to c…
Order-agnostic Identifier for Large Language Model-based Generative Recommendation
Xinyu Lin, Haihan Shi, Wenjie Wang +4
Leveraging Large Language Models (LLMs) for generative recommendation has attracted significant research interest, where item tokenization is a critical step. It involves assigning…