loading
Papers

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Yiming Yang 1 ; Abhimanyu Lad 1 ; Henry Shu 1 ; Bryan Kisiel 1 ; Chad Cumby 2 ; Rayid Ghani 2 and Katharina Probst 2

Affiliations: 1 Carnegie Mellon University, United States ; 2 Accenture Technology Labs, United States

ISBN: 978-989-8111-85-2

Keyword(s): Task-order optimization, Graph structure learning, Link analysis.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Data Engineering ; Enterprise Information Systems ; Health Information Systems ; Human-Computer Interaction ; Industrial Applications of Artificial Intelligence ; Information Systems Analysis and Specification ; Intelligent User Interfaces ; Knowledge Management ; Methodologies, Processes and Platforms ; Model-Driven Software Development ; Ontologies and the Semantic Web ; Society, e-Business and e-Government ; Software Engineering ; Systems Engineering ; Web Information Systems and Technologies

Abstract: In many practical applications, multiple interrelated tasks must be accomplished sequentially through user interaction with retrieval, classification and recommendation systems. The ordering of the tasks may have a significant impact on the overall utility (or performance) of the systems; hence optimal ordering of tasks is desirable. However, manual specification of optimal ordering is often difficult when task dependencies are complex, and exhaustive search for the optimal order is computationally intractable when the number of tasks is large. We propose a novel approach to this problem by using a directed graph to represent partial-order preferences among task pairs, and using link analysis (HITS and PageRank) over the graph as a heuristic to order tasks based on how important they are in reinforcing and propagating the ordering preference. These strategies allow us to find near-optimal solutions with efficient computation, scalable to large applications. We conducted a comparative evaluation of the proposed approach on a form-filling application involving a large collection of business proposals from the Accenture Consulting & Technology Company, using SVM classifiers to recommend keywords, collaborators, customers, technical categories and other related fillers for multiple fields in each proposal. With the proposed approach we obtained near-optimal task orders that improved the utility of the recommendation system by 27% in macro-averaged F1, and 13% in micro-averaged F1, compared to the results obtained using arbitrarily chosen orders, and that were competitive against the best order suggested by domain experts. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.232.55.175

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Yang Y.; Lad A.; Shu H.; Kisiel B.; Cumby C.; Ghani R.; Probst K. and (2009). GRAPH STRUCTURE LEARNING FOR TASK ORDERING.In Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8111-85-2, pages 164-169. DOI: 10.5220/0001989001640169

@conference{iceis09,
author={Yiming Yang and Abhimanyu Lad and Henry Shu and Bryan Kisiel and Chad Cumby and Rayid Ghani and Katharina Probst},
title={GRAPH STRUCTURE LEARNING FOR TASK ORDERING},
booktitle={Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2009},
pages={164-169},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001989001640169},
isbn={978-989-8111-85-2},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - GRAPH STRUCTURE LEARNING FOR TASK ORDERING
SN - 978-989-8111-85-2
AU - Yang, Y.
AU - Lad, A.
AU - Shu, H.
AU - Kisiel, B.
AU - Cumby, C.
AU - Ghani, R.
AU - Probst, K.
PY - 2009
SP - 164
EP - 169
DO - 10.5220/0001989001640169

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.