SimCC-AT: A method to compute similarity of scientific papers with automatic parameter tuning
- Authors
- Hamedani, Masoud Reyhani; Kim, Sang-Wook
- Issue Date
- Jul-2016
- Publisher
- Association for Computing Machinery, Inc
- Keywords
- Automatic weighting; Citations; Content; Contribution score; Similarity
- Citation
- SIGIR 2016 - Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.1005 - 1008
- Indexed
- SCOPUS
- Journal Title
- SIGIR 2016 - Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval
- Start Page
- 1005
- End Page
- 1008
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/154281
- DOI
- 10.1145/2911451.2914715
- 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.
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