Abstract
Automatic citation recommendation based on citation context is a highly valued research topic. When writing papers, researchers can save a lot of time with a system which can recommend a paper list for every citation placeholder. The past works all focus on the content based methods only. In this paper, we consider the citation recommendation as a content based analysis combined with personalization, using users’ publication or citation history as users’ profile and conduct to a personalized citation recommendation. After the combination of users’ citing preference with content relevance measurement, we obtain an 27.65% improvement of the performance in terms of MAP and 31.67% improvement in recall@10 compared with state-of-art models for citation recommendation problem.
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Liu, Y., Yan, R., Yan, H. (2013). Guess What You Will Cite: Personalized Citation Recommendation Based on Users’ Preference. In: Banchs, R.E., Silvestri, F., Liu, TY., Zhang, M., Gao, S., Lang, J. (eds) Information Retrieval Technology. AIRS 2013. Lecture Notes in Computer Science, vol 8281. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45068-6_37
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DOI: https://doi.org/10.1007/978-3-642-45068-6_37
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