SimCC-AT: A Method to Compute Similarity of Scientific Papers with Automatic Parameter Tuning
- Title
- SimCC-AT: A Method to Compute Similarity of Scientific Papers with Automatic Parameter Tuning
- Author
- 김상욱
- Keywords
- Automatic weighting; Citations; Content; Contribution score; Similarity
- Issue Date
- 2016-07
- Publisher
- ACM SIGIR2016
- Citation
- SIGIR 2016 - Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, Page. 1005-1008
- Abstract
- In this paper, we propose SimCC-AT (similarity based on content and citations with automatic parameter tuning) to compute the similarity of scientific papers. As in SimCC, the state-of-the-art method, we exploit a notion of a contribution score in similarity computation. SimCC-AT utilizes an automatic weighting scheme based on SVMrank and thus requires only a smaller number of experiments for parameter tuning than SimCC. Furthermore, our experimental results with a real-world dataset show that the accuracy of SimCC-AT is dramatically higher than that of other existing methods and is comparable to that of SimCC.
- URI
- https://dl.acm.org/citation.cfm?doid=2911451.2914715https://repository.hanyang.ac.kr/handle/20.500.11754/74364
- ISBN
- 978-1-4503-4069-4
- DOI
- 10.1145/2911451.2914715
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
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