Publications (7)
Bridging the Gap: Self-Optimized Fine-Tuning for LLM-based Recommender Systems
Heng Tang, Feng Liu, Xinbo Chen +7
Recent years have witnessed extensive exploration of Large Language Models (LLMs) on the field of Recommender Systems (RS). There are currently two commonly used strategies to enab…
Stable and scalable multistage terahertz-driven particle accelerator
Heng Tang, Lingrong Zhao, Pengfei Zhu +8
Particle accelerators that use electromagnetic fields to increase a charged particle's energy have greatly advanced the development of science and industry since invention. However…
Femtosecond relativistic electron beam with reduced timing jitter from THz-driven beam compression
Lingrong Zhao, Heng Tang, Chao Lu +10
We propose and demonstrate a novel method to reduce the pulse width and timing jitter of a relativistic electron beam through THz-driven beam compression. In this method the longit…
Terahertz oscilloscope for recording time information of ultrashort electron beams
Lingrong Zhao, Zhe Wang, Heng Tang +17
We propose and demonstrate a Terahertz (THz) oscilloscope for recording time information of an ultrashort electron beam. By injecting a laser-driven THz pulse with circular polariz…
EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systems
Yuanqing Yu, Chongming Gao, Jiawei Chen +5
Reinforcement Learning (RL)-Based Recommender Systems (RSs) have gained rising attention for their potential to enhance long-term user engagement. However, research in this field f…
Non-invasive time-sorting in radio-frequency compressed ultrafast electron diffraction
Lingrong Zhao, Jun Wu, Zhe Wang +6
We demonstrate a non-invasive time-sorting method for ultrafast electron diffraction (UED) experiments with radio-frequency (rf) compressed electron beams. We show that electron be…
Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language Model
Yu Cui, Feng Liu, Pengbo Wang +5
Owing to their powerful semantic reasoning capabilities, Large Language Models (LLMs) have been effectively utilized as recommenders, achieving impressive performance. However, the…