Individuation catering recommendation method and system based on multiple targets
A recommendation method and multi-objective technology, applied in the fields of data mining and nutritious and delicious recipes, can solve the problem of not being recommended, and achieve the effect of food diversification and appropriate proportion
Patent Information
- Authority / Receiving Office
- CN · China
- Current Assignee / Owner
- Publication Date
- 2015-06-24
- Estimated Expiration
- Not applicable · inactive patent
Smart Images
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Abstract
Description
Technical field
[0001] The present invention mainly uses a personalized recommendation algorithm based on nutritional elements to accurately recommend their own nutritious and delicious recipes for various groups of people, and belongs to the technical field of data mining. Background technique
[0002] Compared with the past, people's awareness of nutrition and diet has increased, but in the face of many kinds of food, it is difficult for many people to choose the right recipe to meet the real needs of their bodies, so there are many diets that can give users some advice. Suggested websites to help users have a healthy body.
[0003] Three Meals Food Network: There are several functional modules such as gourmet recipes, catering delicacies, gourmet special topics, and food encyclopedias. Topics, such as breakfast, lunch, dinner recipes, etc., show specific introductions of various recipes, including a large number of food demonstrations for each type of recipe and their st...
Examples
Embodiment Construction
[0020] In conjunction with the accompanying drawings, the multi-objective-based personalized catering recommendation method and system are described in detail.
[0021] figure 1 It is a frame diagram of the present invention, mainly composed of three parts: user information, user model, and recipe recommendation model.
[0022] 1. User Information
[0023] The model includes user basic information, user diet records, and user-related recipe web browsing behavior. The user's basic information includes information such as the user's name, gender, weight, and labor intensity, so that the theoretical intake of the user's nutritional elements can be determined. The user's diet record module mainly records the diet of the user's three meals a day, such as recording that user A ate soy milk, sesame seed cakes for breakfast, and dumplings for lunch. User-related recipe page browsing behavior mainly refers to the user's click rate on a recipe page and keyword queries related to reci...