期刊
INFORMATION RETRIEVAL JOURNAL
卷 24, 期 3, 页码 175-204出版社
SPRINGER
DOI: 10.1007/s10791-021-09390-8
关键词
Reviewer assignment; Semantic-based model; Word-based model
资金
- National High Technology Research and Development Program [2017YFB1401903]
- National Natural Science Foundation of China [61876001, 61602003, 61673020]
- Provincial Natural Science Foundation of Anhui Province [1708085QF156]
- Recruitment Project of Anhui University for Academic and Technology Leader
This paper proposes an iterative model based on words and semantics to address the constraints of reviewer assignment, successfully improving recommendation accuracy.
Assigning appropriate reviewers to a manuscript from a pool of candidate reviewers is a common challenge in the academic community. Current word- and semantic-based approaches treat the reviewer assignment problem (RAP) as an information retrieval problem but do not take into account two constraints of the RAP: incompleteness of the reviewer data and interference from nonmanuscript-related papers. In this paper, a word and semantic-based iterative model (WSIM) is proposed to account for the constraints of the RAP by improving the similarity calculations between reviewers and manuscripts. First, we use the improved language model and topic model to extract word features and semantic features to represent reviewers and manuscripts. Second, we use a similarity metric based on the normalized discounted cumulative gain (NDCG) to measure semantic similarity. This metric ignores the probability value (quantitative exact value) of the topic and considers only the ranking (qualitative relevance), thus reducing overfitting to incomplete reviewer data. Finally, we use an iterative model to reduce the interference from nonmanuscript-related papers in the reviewer data. This approach considers the similarity between the manuscript and each of the reviewer's papers. We evaluate the proposed WSIM on two real datasets and compare its performance to that of seven existing methods. The experimental results show that the WSIM improves the recommendation accuracy by at least 2.5% on the top 20.
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