Meal Preference Extraction and Its Rating Scale for Diet Analysis Using Associative Mining

In this paper, we proposed a method to obtain the meal preference for diet requirement analysis targeted for restaurant customers and evaluate them using the proposed recommendation scale. We applied associative mining and filter the mining result and extracted the preferred meal in form of workflows from a universal workflow. The workflow represents dietary preferences in terms of combination of meals taken in different categories. Once we extracted a workflow, we evaluated the workflow with a rating scale to show the recommendation strength quantitatively. Finally, we gave a comparison of the method effectiveness by comparing the extraction preference rating scale with a conventional menu which is based on frequently ordered meals.