Clustering for Similar Recipes in User-Generated Recipe Sites Based on Main Ingredients and Main Seasoning

Today, many people are constantly using user-generated recipe sites when they prepare their meals. Users search for recipes for their meals from the recipe sites. However, when they search for a recipe using an ingredient name, numerous similar pages which are coincidentally similar or which have been plagiarized are found for them. Moreover, a user who searches for a recipe usually does not select a high-ranking recipe from the search results, reacting better than one might with usual web searches. Given many similar recipes included in the search results, it appears to be difficult for them to compare multiple recipes. Actually, when users compare similar recipes, they must better understand the different points of similar recipes. That need for comparison imposes a great burden on users. Therefore, a system classifying the results of user searches according to similar pages in real time would be beneficial for users. In this paper, we propose a clustering method for user-generated recipe sites based on page structure and main ingredient and main seasoning of the food. It provides a means of classifying the user search results according to similar pages. We conducted an experiment to measure the benefits of our proposed method. The experiment results presents the benefits of our proposed method, which classifies similar recipes based on the main ingredients and main seasonings.