A Discriminative Approach to Topic-Based Citation Recommendation

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 RBMCS, 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.