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- Title
Evaluation of Information Retrieval Based Models for Recommendation of Paper Reviewers.
- Authors
Shih-Chieh Wei; Hsin-Yu Luo
- Abstract
As more e-journals appear and the e-review process becomes more popular, the demand for automatic recommendation of a good peer reviewer has been ever increasing. To automate the process of paper reviewer recommendation, this work evaluates four kinds of paper representations, which include full text, abstract, title, and author defined keywords. To match reviewers with papers, this work evaluates seven scoring methods including three topic-based methods from OpenConf, a popular online submission system with source, and four similarity-based methods from the vector space model of traditional information retrieval. The results of the 28 experiments show that recommendation methods based on the vector space model are better than the three topic-based methods of OpenConf in most document representations. Among them, the abstract paper representation combined with cosine similarity matching measure has the highest average precision.
- Subjects
VECTOR analysis; ELECTRONIC journals; CONTENT analysis; INFORMATION retrieval; INFORMATION resources management; RESOURCE management; TECHNICAL writing; RESEARCH papers (Students); TEXT processing (Computer science)
- Publication
Journal of Educational Media & Library Sciences, 2008, Vol 45, Issue 4, p425
- ISSN
1013-090X
- Publication type
Article