Invited Talks:

The EARL workshop proudly presents 2 invited talks, who will provide in-depth insights into the latest developments in the integration of large language models with recommendation systems.

• Prof. Scott Sanner (ssanner@mie.utoronto.ca), from University of Toronto, has a broad range of topics from the data-driven fields of Machine Learning and Information Retrieval to the decision-driven fields of Artificial Intelligence and Operations Research.
• Assoc. Prof. Yongfeng Zhang (yongfeng.zhang@rutgers.edu), from Rutgers University, focuses on a wide range of subjects, including Machine Learning, Data Mining, Information Retrieval, Recommender Systems, and Explainable AI..

Coffee Break:

A central feature of the EARL workshop is the interactive session, coupled with a coffee break. This session is not merely an opportunity to delve into specific research topics, but also a perfect setting for attendees to network and engage in meaningful discussions, fostering a collaborative environment.

Accepted Papers:

• Leveraging LLMs to Enhance a Web-Scale Webpage Recommendation System
  Jaidev Shah, Iman Barjasteh, Amey Barapatre, Rana Forsati, Gang Luo, Fan Wu, Julie Fang, Xue Deng, Blake Shepard, Ronak Shah, Linjun Yang, Hongzhi Li

• GenRec: Generative Sequential Recommendation with Large Language Models
  Panfeng Cao, Pietro Liò

• A Practice-Friendly LLM-Enhanced Paradigm with Preference Parsing for Sequential Recommendation
  Dugang Liu, Shenxian Xian, Xiaolin Lin, Xiaolian Zhang, Hong Zhu, Yuan Fang, Zhen Chen, Zhong Ming

• Data Imputation Using Large Language Model to Accelerate Recommender System
  Jiahao Tian, Jinman Zhao, Zhenkai Wang, Zhicheng Ding, Siyang Li