论文信息 - SimCS: An Effective Method to Compute Similarity of Scientific Papers Based on Contribution Scores - 字舞流文

SimCS: An Effective Method to Compute Similarity of Scientific Papers Based on Contribution Scores

This research was supported by the NRF grant (NRF-2014R1A2A1A10054151) and the ITRC support program (IITP-2015-H8501-15-1013).

Masoud Reyhani Hamedani | Sang-Wook Kim | Sang-Wook Kim | M. Hamedani

[1] Yannick Assogba,et al. Detecting outlier sections in us congressional legislation , 2011, SIGIR.

[2] Min-Yen Kan,et al. Scholarly paper recommendation via user's recent research interests , 2010, JCDL '10.

[3] Hinrich Schütze,et al. Introduction to information retrieval , 2008 .

[4] Nathalie Aussenac-Gilles,et al. Combining Link and Content Information for Scientific Topics Discovery , 2008, 2008 20th IEEE International Conference on Tools with Artificial Intelligence.

[5] Yizhou Sun,et al. P-Rank: a comprehensive structural similarity measure over information networks , 2009, CIKM.

[6] ChengXiang Zhai,et al. When documents are very long, BM25 fails! , 2011, SIGIR.

[7] Seok-Ho Yoon,et al. On computing text-based similarity in scientific literature , 2011, WWW.

[8] Dong-Jin Kim,et al. On exploiting content and citations together to compute similarity of scientific papers , 2013, CIKM.

[9] Thorsten Joachims,et al. Optimizing search engines using clickthrough data , 2002, KDD.

[10] Filippo Menczer,et al. Combining link and content analysis to estimate semantic similarity , 2004, WWW Alt. '04.

[11] Jennifer Widom,et al. SimRank: a measure of structural-context similarity , 2002, KDD.