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20242026
most citedERAGent: Enhancing Retrieval-Augmented Language Models with Improved Accuracy, Efficiency, and Personalization

16 citations · 25 across the 14 of their papers we have counts for

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5 papers · 1 filter

cs.IR2026

Adapting Embedding Models for Agent Capability Retrieval

Tingwei Chen, Yunxiao Shi, Zhengdong Chu +2

Open agent marketplaces list native agents, tool bundles, and reusable skill packages in the same search interface, yet practitioners still have little guidance on how to retrieve…

cs.IR2025

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models

Yunxiao Shi, Shuo Yang, Haimin Zhang +4

Neural Collaborative Filtering models are widely used in recommender systems but are typically trained under static settings, assuming fixed data distributions. This limits their a…

cs.IR2025

PersonaX: A Recommendation Agent Oriented User Modeling Framework for Long Behavior Sequence

Yunxiao Shi, Wujiang Xu, Zeqi Zhang +3

User profile embedded in the prompt template of personalized recommendation agents play a crucial role in shaping their decision-making process. High-quality user profiles are esse…

cs.IR2024

A Learnable Agent Collaboration Network Framework for Personalized Multimodal AI Search Engine

Yunxiao Shi, Min Xu, Haimin Zhang +2

Large language models (LLMs) and retrieval-augmented generation (RAG) techniques have revolutionized traditional information access, enabling AI agent to search and summarize infor…

cs.IR2024★ 3 cited

SLMRec: Distilling Large Language Models into Small for Sequential Recommendation

Wujiang Xu, Qitian Wu, Zujie Liang +5

Sequential Recommendation (SR) task involves predicting the next item a user is likely to interact with, given their past interactions. The SR models examine the sequence of a user…