AI Agent for Information Retrieval: Generating and Ranking

The field of information retrieval has significantly transformed with the integration of AI technologies. AI agents, especially those leveraging LLMs and vast computational power, have revolutionized information retrieval, processing, and presentation. LLM agents, with advanced memory, reasoning, and planning capabilities, can perform complex tasks, engage in coherent conversations, and provide personalized responses. Despite these advancements, challenges such as ensuring relevance and accuracy, mitigating biases, providing real-time responses, and maintaining data security remain. This workshop aims to explore these challenges, share innovative solutions, and discuss future directions. It will provide a platform to bring together researchers, practitioners to discuss the latest theoretical advancements and practical implementations of AI agents in information retrieval. Topics include AI in search, recommendation, and personalization systems. By gathering a diverse group of experts, the workshop seeks to deepen the understanding of AI agents in information retrieval, advance the field, and enhance its societal impact. Participants will gain insights into cutting-edge research, emerging trends, and foster knowledge exchange and collaboration within the community.

Authors

Institutions

Publication Details

Published
2024-10-20
DOI
https://doi.org/10.1145/3627673.3680120
Citations
7
Primary Topic
Data Mining Algorithms and Applications
Type
article
Field-Weighted Citation Impact
5.45
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI Agent for Information Retrieval: Generating and Ranking

Wei Liu, Linsey Pang, Philip S. Yu, Qingsong Wen et al.
7 citations
Data Mining Algorithms and Applications
5.45
article

AI Agent for Information Retrieval: Generating and Ranking

Wei Liu, Linsey Pang, Philip S. Yu, Qingsong Wen, Zhiwei Liu, Yongfeng Zhang
article en
7 citations

Abstract

The field of information retrieval has significantly transformed with the integration of AI technologies. AI agents, especially those leveraging LLMs and vast computational power, have revolutionized information retrieval, processing, and presentation. LLM agents, with advanced memory, reasoning, and planning capabilities, can perform complex tasks, engage in coherent conversations, and provide personalized responses. Despite these advancements, challenges such as ensuring relevance and accuracy, mitigating biases, providing real-time responses, and maintaining data security remain. This workshop aims to explore these challenges, share innovative solutions, and discuss future directions. It will provide a platform to bring together researchers, practitioners to discuss the latest theoretical advancements and practical implementations of AI agents in information retrieval. Topics include AI in search, recommendation, and personalization systems. By gathering a diverse group of experts, the workshop seeks to deepen the understanding of AI agents in information retrieval, advance the field, and enhance its societal impact. Participants will gain insights into cutting-edge research, emerging trends, and foster knowledge exchange and collaboration within the community.

Rutgers, The State University of New Jersey (US), University of Technology Sydney (AU), University of Illinois Chicago (US), Salesforce (United States) (US)
Openalex Percentile: Top 4%
Data Mining Algorithms and Applications
5.45
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

AI Agent for Information Retrieval: Generating and Ranking — Wei Liu, Linsey Pang, et al. · (2024) | TGRS Research Map | TGRS