7 citations · 9 across the 6 of their papers we have counts for
6 papers
ClawRec: A Claw-Native Recommender System
Chenghao Wu, Kesha Ou, Xiaolei Wang +8
Recommender systems have become integral to navigating the modern digital ecosystem. Yet most deployed systems remain confined within single-platform boundaries, observing localize…
Improving LLM-based Recommendation with Self-Hard Negatives from Intermediate Layers
Bingqian Li, Bowen Zheng, Xiaolei Wang +5
Large language models (LLMs) have shown great promise in recommender systems, where supervised fine-tuning (SFT) is commonly used for adaptation. Subsequent studies further introdu…
RecNet: Self-Evolving Preference Propagation for Agentic Recommender Systems
Bingqian Li, Xiaolei Wang, Junyi Li +5
Agentic recommender systems leverage Large Language Models (LLMs) to model complex user behaviors and support personalized decision-making. However, existing methods primarily mode…
LLMBox: A Comprehensive Library for Large Language Models
Tianyi Tang, Yiwen Hu, Bingqian Li +22
To facilitate the research on large language models (LLMs), this paper presents a comprehensive and unified library, LLMBox, to ease the development, use, and evaluation of LLMs. T…
Sequence-level Semantic Representation Fusion for Recommender Systems
Lanling Xu, Zhen Tian, Bingqian Li +4
With the rapid development of recommender systems, there is increasing side information that can be employed to improve the recommendation performance. Specially, we focus on the u…
Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis
Lanling Xu, Junjie Zhang, Bingqian Li +4
Recently, Large Language Models~(LLMs) such as ChatGPT have showcased remarkable abilities in solving general tasks, demonstrating the potential for applications in recommender sys…