activity
20222026
most citedRepresentation Learning with Large Language Models for Recommendation

215 citations · 725 across the 19 of their papers we have counts for

collaborators
Showing 2025Show all

6 papers · 1 filter

cs.SE2025

DeepCode: Open Agentic Coding

Zongwei Li, Zhonghang Li, Zirui Guo +2

Recent advances in large language models (LLMs) have given rise to powerful coding agents, making it possible for code assistants to evolve into code engineers. However, existing m…

cs.AI2025

RAG-Anything: All-in-One RAG Framework

Zirui Guo, Xubin Ren, Lingrui Xu +2

Retrieval-Augmented Generation (RAG) has emerged as a fundamental paradigm for expanding Large Language Models beyond their static training limitations. However, a critical misalig…

cs.IR2025

RecGPT: A Foundation Model for Sequential Recommendation

Yangqin Jiang, Xubin Ren, Lianghao Xia +3

This work addresses a fundamental barrier in recommender systems: the inability to generalize across domains without extensive retraining. Traditional ID-based approaches fail enti…

cs.CY2025★ 15 cited

Urban Computing in the Era of Large Language Models

Zhonghang Li, Lianghao Xia, Xubin Ren +4

Urban computing has emerged as a multidisciplinary field that harnesses data-driven technologies to address challenges and improve urban living. Traditional approaches, while benef…

cs.IR2025

VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context Videos

Xubin Ren, Lingrui Xu, Long Xia +3

Retrieval-Augmented Generation (RAG) has demonstrated remarkable success in enhancing Large Language Models (LLMs) through external knowledge integration, yet its application has p…

cs.AI2025★ 1 cited

MiniRAG: Towards Extremely Simple Retrieval-Augmented Generation

Tianyu Fan, Jingyuan Wang, Xubin Ren +1

The growing demand for efficient and lightweight Retrieval-Augmented Generation (RAG) systems has highlighted significant challenges when deploying Small Language Models (SLMs) in…