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Erschienen in:
2022 | OriginalPaper | Buchkapitel
SolutionTailor: Scientific Paper Recommendation Based on Fine-Grained Abstract Analysis
verfasst von : Tetsuya Takahashi, Marie Katsurai
Erschienen in: Advances in Information Retrieval
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Abstract
Locating specific scientific content from a large corpora is crucial to researchers. This paper presents SolutionTailor (The demo video is available at: https://mm.doshisha.ac.jp/sci2/SolutionTailor.html), a novel system that recommends papers that provide diverse solutions for a specific research objective. The proposed system does not require any prior information from a user; it only requires the user to specify the target research field and enter a research abstract representing the user’s interests. Our approach uses a neural language model to divide abstract sentences into “Background/Objective” and “Methodologies” and defines a new similarity measure between papers. Our current experiments indicate that the proposed system can recommend literature in a specific objective beyond a query paper’s citations compared with a baseline system.