Abstract Information Extraction (IE) aims to extract structural knowledge from plain natural
language texts. Recently, generative Large Language Models (LLMs) have demonstrated …

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

The recent surge in Large Language Models (LLMs) has garnered significant attention
across numerous fields. Fine-tuning is often required to fit general LLMs for a specific …

Recently, large language models such as ChatGPT have showcased remarkable abilities in
solving general tasks, demonstrating the potential for applications in recommender systems …

With the rise of large language models (LLMs), recent works have leveraged LLMs to
improve the performance of click-through rate (CTR) prediction. However, we argue that a …

As the demand for more personalized recommendation grows and a dramatic boom in
commercial scenarios arises, the study on multi-scenario recommendation (MSR) has …

Recently, Foundation Models (FMs), with their extensive knowledge bases and complex
architectures, have offered unique opportunities within the realm of recommender systems …

Dissolution refers to the process in which solvent molecules and solute molecules attract
and combine with each other. The extensive solubility data generated from the dissolution of …

Conclusion This letter investigates the sample efficiency property of recommender systems
enhanced by large language models. We propose a simple yet effective framework (ie …

Sequential recommender systems (SRS) aim to predict users' subsequent choices based on
their historical interactions and have found applications in diverse fields such as e …