Abstract
Citation recommendation is a meaningful and challenging research problem nowadays. Most of prior researches make a simplified assumption that the citations are more preferential for the papers to cite than the uncited ones. Consequently, the unreasonable priority assertion between the cited and uncited papers derived from the above assumption makes citation recommendation prone to be biased. To address this issue, we firstly propose an instinctive assumption that the more preferential a reference is, the easier it can be recognized as a citation. Based on this assumption, we propose two methods CR and CR+C aiming to find more unbiased priority between the cited and uncited papers with c-SVC. Then, a improved RankSVM model is trained for citation ranking purpose. Experimental results demonstrate that, comparing with the RankSVM model, our methods achieve 5.27% improvement on Recall@50 and 8.28% improvement on MRR. Moreover, CR+C achieves advantage on efficiency by taking only 18.9% time it needs.
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Lu, WY., Yang, YB., Mao, XJ., Zhu, QH. (2015). Effective Citation Recommendation by Unbiased Reference Priority Recognition. In: Cheng, R., Cui, B., Zhang, Z., Cai, R., Xu, J. (eds) Web Technologies and Applications. APWeb 2015. Lecture Notes in Computer Science(), vol 9313. Springer, Cham. https://doi.org/10.1007/978-3-319-25255-1_44
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DOI: https://doi.org/10.1007/978-3-319-25255-1_44
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