Search Results for author:
Found 2 papers, 2 papers with code
Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language Model
Owing to their powerful semantic reasoning capabilities, Large Language Models (LLMs) have been effectively utilized as recommenders, achieving impressive performance.
EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systems
EasyRL4Rec seeks to facilitate the model development and experimental process in the domain of RL-based RSs.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
