Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (9): 1443-1450.

### Intelligent Recommendation Method of Sous-Vide Cooking Dishes Correlation Analysis based on Association Rules Mining

Xing Qiaoa,*, Liang Luob, Jinjun Yanga, and Zongbo Huc

1. aSichuan Tourism University, Chengdu, 610100, China;
bUniversity of Electronic Science and Technology of China, Chengdu, 610100, China;
cSichuan Branch of Allinpay Network Service Co., Ltd., Chengdu, 610100, China
• Submitted on  ;  Revised on  ; Accepted on
• Contact: * E-mail address: joeqiao1@163.com
• About author:Xing Qiao is a Lecturer of Sichuan Tourism University. His research interests include cooking and nutrition, food and beverage management.
Jinjun Yang is a culinary experimenter of Sichuan Tourism University. He is engaged in the management of cooking laboratories and the intelligent management of research.
Zongbo Hu is currently a software engineer and has in-depth research in e-commerce and big data, majoring in computer technology and science.

Abstract: With the advent of the era of big data, a large number of information data are generated at every moment. When people are faced with a large amount of information, they are often overwhelmed in the ocean of data and unable to make a quick and accurate decision. In this context, a personalized cloud computing recommendation system emerges as the times require, which can solve the problem of information overload. The development of the catering industry and the mutual penetration of the catering culture meet people's requirements for the diversity of food. However, with the substantial growth of the number of dishes and the emergence of new products, diners will also encounter difficulties in choosing a large number of dishes, especially sous-vide cooking dishes. In this paper, we choose the recommendation mechanism based on association rules to study the association information between diners and dishes. The Apriori algorithm is selected to mine the potential association information between dishes from a large number of historical orders, and the frequent pattern set of associated dishes is obtained as the association rule set. According to the characteristics and advantages of Sous vide cooking dishes, and then according to the dishes that the diner has ordered, through the cloud computing, the mining association rules recommend dishes that meet the diner's taste.