The powerful large language models (LLMs) have played a pivotal role in advancing
recommender systems. Recently, in both academia and industry, there has been a surge of …

This paper presents OpenP5, an open-source library for benchmarking foundation models
for recommendation under the Pre-train, Personalized Prompt and Predict Paradigm (P5) …

Large language models (LLMs) have significantly influenced recommender systems,
spurring interest across academia and industry in leveraging LLMs for recommendation …

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

In recent years, the integration of Large Language Models (LLMs) into recommender
systems has garnered interest among both practitioners and researchers. Despite this …

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

Privacy-preserving methods for personalizing large language models (LLMs) are relatively
under-explored. There are two schools of thought on this topic:(1) generating personalized …

Generating recommendation reasons for recommendation results is a long-standing
problem because it is challenging to explain the underlying reasons for recommending an …

With the information explosion on the Web, search and recommendation are foundational
infrastructures to satisfying users' information needs. As the two sides of the same coin, both …

Multimodal recommendation aims to recommend user-preferred candidates based on
her/his historically interacted items and associated multimodal information. Previous studies …