To prevent non-communicable diseases, it is important to review consumers' dietary habits. Most existing applications for improving eating habits require users to upload photos of their meals and record them manually, but such procedures are time-consuming and laborious. Because the targets of the proposed method are those who are not highly conscious of their health, it is necessary to make the application easy to use. This study applies the history of supermarket purchases to calculate nutrient intake and recommend foods that improve nutritional balance with the least amount of user input. Consequently, the nutrient intake can be estimated with an acceptable error, and foods that are easy for the user to purchase can be recommended. Because the proposed method does not use artificial intelligence technologies to generate recommendations, the reasons for food recommendations are clear and the computational cost is reduced.