Consensus-Based Evaluation Framework for Cooperative Information Retrieval Systems

Authors:
Jason J. Jung;Geun-Sik Jo
Affiliations:
Intelligent E-Commerce Systems Laboratory, Department of Computer and Information Engineering, Inha University, 253 Yonghyun-dong, Incheon, 402-751, Korea;Intelligent E-Commerce Systems Laboratory, Department of Computer and Information Engineering, Inha University, 253 Yonghyun-dong, Incheon, 402-751, Korea
Venue:
KES-AMSTA '07 Proceedings of the 1st KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Year:
2007

Citing 10
Cited 0

Searching distributed collections with inference networks

SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval

Modern Information Retrieval
Distributed Systems: Concepts and Design

Distributed Systems: Concepts and Design
Consensus system for solving conflicts in distributed systems

Information Sciences—Informatics and Computer Science: An International Journal
"Agents are Everywhere!"

IEEE Internet Computing
Generalizing GlOSS to Vector-Space Databases and Broker Hierarchies

VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
iJADE Web-Miner: An Intelligent Agent Framework for Internet Shopping

IEEE Transactions on Knowledge and Data Engineering
CQE: a collaborative querying environment

Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Ontological framework based on contextual mediation for collaborative information retrieval

Information Retrieval
A consensus-based multi-agent approach for information retrieval in internet

ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

Multi-agent systems have been attacking the challenges of distributed information retrieval. In this paper, we propose a consensus method-based framework to evaluate the performance of cooperative information retrieval tasks of the agents. Two well-known measurements, precisionand recall, are extended to handle consensual closeness (i.e., local and global consensus) between the retrieved results. We show in a motivating example that the proposed criteria are prone to solve the problem of rigidity of classical precisionand recall. More importantly, the retrieved results can be ranked with respect to the consensual score.