2 papers
cs.IR2024
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…
cs.IR2024
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…