About This Site
Similarity-based search engine for scientific literature, using machine learning to find related papers. Citrus is a similarity-based search engine for scientific literature. You select a paper, start the search, and jump right to the heart of your research domain. Get an overview of important contributions from seminal papers to the state of the art. Citrus helps you find relevant articles in a research field with a single search. It allows you to explore closely related research, find relevant work fast, view important contributions at a glance on a timeline, and avoid missing papers using different taxonomy. Behind the scenes, the similarity of papers is computed using graph and text-based machine-learning techniques. Citrus indexes data provided by Semantic Scholar's Open Research Corpus, which spans over 200 Million publications and around 2 Billion citations.
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Key Features AI
Core Features
Similarity-based search for scientific literature
Citation network analysis
Content-based similarity analysis
Timeline view of important contributions
Advantages
Finds relevant papers based on similarity, not just keywords.
Provides an overview of important contributions on a timeline.
Uses machine learning techniques to compute paper similarity.
Indexes a large dataset of scientific publications.
缺点：Requires a seed paper to start the search.
缺点：Similarity depends on the selected similarity measure (Citation Network or Content).
缺点：The site is under active development, so bugs may be present.
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