Traditional recommender systems typically use user-item rating histories as their main data
source. However, deep generative models now have the capability to model and sample …

In the past decades, recommender systems have attracted much attention in both research
and industry communities. Existing recommendation models mainly learn the underlying …

Recommender system plays a vital role in various online services. However, its insulated
nature of training and deploying separately within a specific closed domain limits its access …

Large language model (LLM) based agents have recently attracted much attention from the
research and industry communities. Compared with original LLMs, LLM-based agents are …

With the rapid development of online services and web applications, recommender systems
(RS) have become increasingly indispensable for mitigating information overload and …

Recommendation foundation model utilizes large language models (LLM) for
recommendation by converting recommendation tasks into natural language tasks. It …

Recently, there has been an emergence of employing LLM-powered agents as believable
human proxies, based on their remarkable decision-making capability. However, existing …

Computer Vision (CV), Natural Language Processing (NLP), and Recommender Systems
(RecSys) are three prominent AI applications that have traditionally developed …

Recommender systems (RS), serving at the forefront of Human-centered AI, are widely
deployed in almost every corner of the web and facilitate the human decision-making …

Abstract In recent years, Large Language Models (LLMs) have emerged as powerful tools
for diverse natural language processing tasks. However, their potential for recommender …