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The number of academic papers being published is increasing exponentially inrecent years, and recommending adequate citations to assist researchers inwriting papers is a non-trivial task. Conventional approaches may not beoptimal, as the recommended papers may already be known to the users, or besolely relevant to the surrounding context but not other ideas discussed in themanuscript. In this work, we propose a novel embedding algorithm DocCit2Vec,along with the new concept of ``structural context'', to tackle theaforementioned issues. The proposed approach demonstrates superior performancesto baseline models in extensive experiments designed to simulate practicalusage scenarios.

Citation Recommendations Considering Content and Structural Context Embedding