User's food preference extraction for personalized cooking recipe recommendation

Abstract

Abstract. There are many websites and researches that involve cooking recipe recommendation. However, these websites present cooking recipes on the basis of entry date, access frequency, or the recipe’s user ratings. They do not reflect the user’s personal preferences. We have proposed a personalized recipe recommendation method that is based on the user’s food preferences. For extracting the user’s food preferences, we use his/her recipe browsing and cooking history. In this paper, we present a method for extracting the user’s preferences. In the experimental results, extracting the user’s favorite ingredients were detected with a 60 to 83 % of precision. And extracting the unfavorite ingredients were detected with 14.7 % of precision, and 58 % of recall. Furthermore, the F-measure value for extraction of favorite ingredients was 60.8 % when we focused on the top 20 ingredients. Key words: user’s food preferences, preference extraction, recipe recommendation, cooking and browsing history 1