Abstract
With the tremendous proliferation of scientific literature and research papers published every year, fulfilling a comprehensive literature overview became a tedious and time-consuming task. Citation recommendation is considerably important to improve the efficiency and quality of literature search. It scales down the information overload in academia by using the content of the paper and citation information to automatically recommend papers relevant to the students or respectively researcher’s preferences. In this paper, we propose a novel personalized citation recommendation system comprised of a query-based recommendation module and a graph-based ranking module. The query-based recommendation module relies on Deep Semantic Similarity Model (DSSM) to rank papers based on their semantic similarity to a query text. The graph-based ranking module uses a heterogeneous graph that incorporates the citation and content information within papers, to rank the candidate papers based on their relevance to a query text and the corresponding author. The fusion of the results from both modules provides the final recommendation list. Our intensive experiments on the ACL Analogy dataset (AAN) prove that our model significantly outperforms other state-of-the-art techniques in terms of MAP and MRR. Also it shows a better Recall in the top ranked papers over the best performing baseline.
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Acknowledgments
This work has been co-funded by the German Federal Ministry of Education and Research (BMBF) within the framework of the Software Campus project “PIOBRec” [01IS17050].
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Alkhatib, W., Rensing, C. (2020). Personalized Citation Recommendation Using an Ensemble Model of DSSM and Bibliographic Information. In: Pinkwart, N., Liu, S. (eds) Artificial Intelligence Supported Educational Technologies. Advances in Analytics for Learning and Teaching. Springer, Cham. https://doi.org/10.1007/978-3-030-41099-5_10
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