We develop SolutionTailor, 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.

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Tetsuya Takahashi and Marie Katsurai, “SolutionTailor: Scientific paper recommendation based on fine-grained abstract analysis,” Advances in Information Retrieval. ECIR 2022. Lecture Notes in Computer Science, vol 13186. Springer, Cham, 2022.