A LSTM Based Model for Personalized Context-Aware Citation Recommendation

• Main Authors: Libin Yang, Yu Zheng, Xiaoyan Cai, Hang Dai, Dejun Mu, Lantian Guo, Tao Dai
• Format: Article
• Language: English
• Published: IEEE 2018-01-01
• Series: IEEE Access
• Subjects: Distributed representation; long short-term memory (LSTM); citation context; context-aware citation recommendation; personalized recommendation
• Online Access: https://ieeexplore.ieee.org/document/8478136/

The rapid growth of scientific papers makes it difficult to find relevant and appropriate citations. Context-aware citation recommendation aims to overcome this problem by providing a list of scientific papers given a short passage of text. In this paper, we propose a long-short-term memory (LSTM)based model for context-aware citation recommendation, which first learns the distributed representations of the citation contexts and the scientific papers separately based on LSTM, and then measures the relevance based on the learned distributed representation of citation contexts and the scientific papers. Finally, the scientific papers with high relevance scores are selected as the recommendation list. In particular, we try to incorporate author information, venue information, and content information in scientific paper distributed vector representation. Furthermore, we integrate author information of the given context in citation context distributed vector representation. Thus, the proposed model makes personalized context-aware citation recommendation possible, which is a new issue that few papers addressed in the past. When conducting experiments on the ACL Anthology Network and DBLP data sets, the results demonstrate the proposed LSTM-based model for context-aware citation recommendation is able to achieve considerable improvement over previous context-aware citation recommendation approaches. The personalized recommendation approach is also competitive with the non-personalized recommendation approach.