Citation Recommendation with a Content-Sensitive DeepWalk Based Approach

Systems for recommending scientific papers mainly help researchers to find a list of references that related to the researcher's interest effectively and automatically. Many state-of-the-art technique have been used for recommendation system, however, the traditional approaches has the issues of data scarcities and cold start, and existing recommended approaches with network representation only focus on one aspect of node information and cannot leverage content information. In this paper, we proposed a Citation Recommendation method with a Content-Aware bibliographic network representation, called CR-CA, whose recommended process contains two levels: (1) At the node content level, the proposed approach calculates similarities between the target and candidate papers, selecting an initial seed set of papers; (2) At the citation network structure level, this approach exploits citation relationship between papers to study latent representation of the scientific papers based on a deep natural language method-DeepWalk. The proposed approach was tested on the AAN dataset demonstrate that this approach outperforms baseline algorithms, in the true positive rate (Recall) and normalized discounted cumulative gain (NDCG).