Context-Based Collaborative Filtering for Citation Recommendation
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论文类型：期刊论文
发表时间：2015-01-01
发表刊物：IEEE ACCESS
收录刊物：Scopus、EI、SCIE
文献类型：J
卷号：3
页面范围：1695-1703
ISSN号：2169-3536
关键字：Citation recommendation; collaborative filtering; citation context; citation relation matrix; association mining
摘要：Citation recommendation is an interesting and significant research area as it solves the information overload in academia by automatically suggesting relevant references for a research paper. Recently, with the rapid proliferation of information technology, research papers are rapidly published in various conferences and journals. This makes citation recommendation a highly important and challenging discipline. In this paper, we propose a novel citation recommendation method that uses only easily obtained citation relations as source data. The rationale underlying this method is that, if two citing papers are significantly co-occurring with the same citing paper(s), they should be similar to some extent. Based on the above rationale, an association mining technique is employed to obtain the paper representation of each citing paper from the citation context. Then, these paper representations are pairwise compared to compute similarities between the citing papers for collaborative filtering. We evaluate our proposed method through two relevant real-world data sets. Our experimental results demonstrate that the proposed method significantly outperforms the baseline method in terms of precision, recall, and F1, as well as mean average precision and mean reciprocal rank, which are metrics related to the rank information in the recommendation list.