Two stages data mining analytics for food intentional and behavioral recommendations

Abstract

A recommender system is an information filtering system used to predict a user’s rating or preference for an item. Dietary preferences are often influenced by various etiquettes and culture, such as appetite, the selection of ingredients, menu development, cooking methods, choice of tableware, seating arrangement of diners, order of eating, etc. Food delivery service is a courier service in that delivers food to customers by restaurants, stores, or independent delivery companies. With the continuous advances in information systems and data science, recommender systems are gradually developing towards to intentional and behavioral recommendations. Behavioral recommendation is an extension of peer-to-peer recommendation, where merchants find the people who want to buy the product and deliver it. Intentional recommendation is a mindset that seeks to understand the life of consumers; by continuously collecting information about their actions on the internet and displaying events and information that match the life and purchase preferences of consumers. This study considers that data targeting is a method by which food delivery service platforms can understand consumers’ dietary preferences and individual lifestyles so that the food delivery service platform can effectively recommend food to the consumer. Thus, this study implements two stages data mining analytics, including clustering analysis and association rules, to investigate Taiwanese food consumers (n= 2,138) to investigate dietary and food delivery services behaviors and preferences to find knowledge profiles/patterns/rules for food intentional and behavioral recommendations. Finally, discussion and implications are presented.