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In user-generated recipe websites, users post their-original recipes. Somerecipes, however, are very similar in major components such as the cookinginstructions to other recipes. We refer to such recipes as "near-duplicaterecipes". In this study, we propose a method that extends the "Word Mover'sDistance", which calculates distances between texts based on word embedding, tocharacter 3-gram embedding. Using a corpus of over 1.21 million recipes, welearned the word embedding and the character 3-gram embedding by using aSkip-Gram model with negative sampling and fastText to extract candidate pairsof near-duplicate recipes. We then annotated these candidates and evaluated theproposed method against a comparison method. Our results demonstrated thatnear-duplicate recipes that were not detected by the comparison method weresuccessfully detected by the proposed method.

Character 3-gram Mover's Distance: An Effective Method for Detecting Near-duplicate Japanese-language Recipes