A Neural Probabilistic Model for Context Based Citation Recommendation


  • Wenyi Huang The Pennsylvania State University
  • Zhaohui Wu The Pennsylvania State University
  • Chen Liang The Pennsylvania State University
  • Prasenjit Mitra The Pennsylvania State University
  • C. Giles The Pennsylvania State University




Citation recommendation, Neural Probabilistic Model, Distributed Representations


Automatic citation recommendation can be very useful for authoring a paper and is an AI-complete problem due to the challenge of bridging the semantic gap between citation context and the cited paper. It is not always easy for knowledgeable researchers to give an accurate citation context for a cited paper or to find the right paper to cite given context. To help with this problem, we propose a novel neural probabilistic model that jointly learns the semantic representations of citation contexts and cited papers. The probability of citing a paper given a citation context is estimated by training a multi-layer neural network. We implement and evaluate our model on the entire CiteSeer dataset, which at the time of this work consists of 10,760,318 citation contexts from 1,017,457 papers. We show that the proposed model significantly outperforms other state-of-the-art models in recall, MAP, MRR, and nDCG.




How to Cite

Huang, W., Wu, Z., Liang, C., Mitra, P., & Giles, C. (2015). A Neural Probabilistic Model for Context Based Citation Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). https://doi.org/10.1609/aaai.v29i1.9528