Citation Recommendations Considering Content and Structural Context Embedding
This work addresses the challenge of efficient citation recommendation for researchers, though it appears incremental as it builds on existing embedding methods with a new structural context concept.
The paper tackled the problem of recommending relevant citations for academic papers by proposing DocCit2Vec, a novel embedding algorithm that incorporates structural context, and it demonstrated superior performance over baseline models in experiments simulating practical usage.
The number of academic papers being published is increasing exponentially in recent years, and recommending adequate citations to assist researchers in writing papers is a non-trivial task. Conventional approaches may not be optimal, as the recommended papers may already be known to the users, or be solely relevant to the surrounding context but not other ideas discussed in the manuscript. In this work, we propose a novel embedding algorithm DocCit2Vec, along with the new concept of ``structural context'', to tackle the aforementioned issues. The proposed approach demonstrates superior performances to baseline models in extensive experiments designed to simulate practical usage scenarios.