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Conference Papers Year : 2020

Scientific Paper Recommendation Using Author’s Dual Role Citation Relationship

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Abstract

Vector representations learning (also known as embeddings) for users (items) are at the core of modern recommendation systems. Existing works usually map users and items to low-dimensional space to predict user preferences for items and describe pre-existing features (such as ID) of users (or items) to obtain the embedding of the user (or item). However, we argue that such methods neglect the dual role of users, side information of users and items (e.g., dual citation relationship of authors, authoritativeness of authors and papers) when recommendation is performed for scientific paper. As such, the resulting representations may be insufficient to predict optimal author citations.In this paper, we contribute a new model named scientific paper recommendation using Author’s Dual Role Citation Relationship (ADRCR) to capture authors’ citation relationship. Our model incorporates the reference relation between author and author, the citation relationship between author and paper, and the authoritativeness of authors and papers into a unified framework. In particular, our model predicts author citation relationship in each specific class. Experiments on the DBLP dataset demonstrate that ADRCR outperforms state-of-the-art recommendation methods. Further analysis shows that modeling the author’s dual role is particularly helpful for providing recommendation for sparse users that have very few interactions.
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Dates and versions

hal-03456971 , version 1 (30-11-2021)

Licence

Attribution - CC BY 4.0

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Donglin Hu, Huifang Ma, Yuhang Liu, Xiangchun He. Scientific Paper Recommendation Using Author’s Dual Role Citation Relationship. 11th International Conference on Intelligent Information Processing (IIP), Jul 2020, Hangzhou, China. pp.121-132, ⟨10.1007/978-3-030-46931-3_12⟩. ⟨hal-03456971⟩
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