Authors:
Kyosuke Ikejiri
;
Yuichi Sei
;
Hiroyuki Nakagawa
;
Yasuyuki Tahara
and
Akihiko Ohsuga
Affiliation:
University of Electro-Communications, Japan
Keyword(s):
Data Mining, Recipe, Information Recommendation.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Natural Language Processing
;
Pattern Recognition
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
Abstract:
Many surprising recipes that utilize different ingredients or cooking processes from normal recipes exist on
user-generated recipe sites. The easiest way to find surprising recipes is to use the search function of the recipe
sites. However, the titles of surprising recipes do not always include a keyword, such as “surprise”, or an
indication that a recipe is unusual in any way. Therefore, we cannot find surprising recipes very easily. In this
paper, we propose a method to extract surprising or unique recipes from those user-generated recipe sites. We
propose an RF-IIF (Recipe Frequency-Inverse Ingredient Frequency) based on TF-IDF (Term Frequency-
Inverse Ingredient Frequency). First, we calculate the surprising value of the ingredients by using RF-IIF.
Then, we calculate the surprising value of each recipe by summing the surprising values of the ingredients that
appear in a recipe. Finally, we extract recipes that have high surprising values as surprising recipes of the dish
categor
y. In the evaluation experiment, the subjects requested an evaluation about each surprising recipe. As a
result, we showed that the extracted recipes were valid recipes and also had a surprising or unusual element.
Therefore, we showed the usefulness of the proposed method.
(More)