Abstract
In this paper, we present a study of a novel problem, i.e. topic-based citation recommendation, which involves recommending papers to be referred to. Traditionally, this problem is usually treated as an engineering issue and dealt with using heuristics. This paper gives a formalization of topic-based citation recommendation and proposes a discriminative approach to this problem. Specifically, it proposes a two-layer Restricted Boltzmann Machine model, called RBM-CS, which can discover topic distributions of paper content and citation relationship simultaneously. Experimental results demonstrate that RBM-CS can significantly outperform baseline methods for citation recommendation.
The work is supported by the National Natural Science Foundation of China (60703059), Chinese National Key Foundation Research and Development Plan (2007CB310803), and Chinese Young Faculty Research Funding (20070003093).
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Tang, J., Zhang, J. (2009). A Discriminative Approach to Topic-Based Citation Recommendation . In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, TB. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2009. Lecture Notes in Computer Science(), vol 5476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01307-2_55
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DOI: https://doi.org/10.1007/978-3-642-01307-2_55
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