RecSys 2025(Extended Abstract)に当研究室の論文が採録

■書誌情報
Irene Li, Ruihai Dong, Guillaume Salha-Galvan, Aonghus Lawlor, Dairui Liu, Lei Li: EARL: The 2nd Workshop on Evaluating and Applying Recommender Systems with Large Language Models, Proceedings of the 19th ACM Conference on Recommender Systems (RecSys 2025), Extended Abstract, September 2025
■概要
 This article presents our proposal to organize the 2nd Workshop on Evaluating and Applying Recommender Systems with Large Language Models (EARL), to be held in conjunction with the 19th ACM Conference on Recommender Systems (RecSys 2025) in Prague, Czech Republic, in September 2025. Building on the success of the first EARL edition at RecSys 2024, we will foster dynamic and interactive discussions and debates on the application and evaluation of large language models (LLMs) in recommender systems (RSs). The workshop will explore emerging techniques such as retrieval-augmented generation (RAG), multi-modal recommendation, reinforcement learning with human feedback (RLHF), scalable fine-tuning methods, dynamic prompting strategies, and personalized conversational agents. In addition to highlighting key innovations and showcasing applications across diverse sectors, we will emphasize critical challenges in LLM-driven personalization, including bias, fairness, and transparency, which are essential for ensuring trustworthy and responsible RSs. Ultimately, the workshop aims to inspire new research directions and foster collaboration in the rapidly evolving field of LLM-powered RSs.