Personalized diet recommendation method and system based on knowledge graph

A technology of knowledge graph and recommendation method, applied in the field of personalized diet recommendation method and system, can solve the problem of low accuracy of recipe recommendation

Pending Publication Date: 2021-05-18
吾征智能技术(北京)有限公司
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Problems solved by technology

[0003] In view of this, the present invention proposes a personalized diet recommendation method and system based on knowle

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  • Personalized diet recommendation method and system based on knowledge graph
  • Personalized diet recommendation method and system based on knowledge graph
  • Personalized diet recommendation method and system based on knowledge graph

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Embodiment Construction

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the implementation manners in the present invention, all other implementation manners obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0062] see figure 1 , the present invention proposes a personalized diet recommendation method based on a knowledge map, the method comprising:

[0063] S1. Obtain recipe data in the knowledge base, extract recipe attribute information, and construct recipe entity nodes;

[0064] Specifically, the recipe attribute information involves the category of dishes (hot dishes, cold dishes, vegetarian dishes, etc.), efficacy (wind-cold, pain relief, qi de...

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Abstract

The invention discloses a personalized diet recommendation method and system based on a knowledge graph. The method comprises the steps: acquiring menu data of a knowledge base, extracting attribute information of a menu, and constructing a menu entity node; acquiring existing user historical behavior data, calculating similarity between menus, and generating a menu knowledge graph according to the similarity; according to the menu knowledge graph, generating a first recommendation candidate set with high similarity with the historical behavior data of the user; calculating the tendency degree of the user to the menu attributes, and generating a relation graph of the user and the menu attributes; screening out an optimized second recommendation candidate set according to a relation graph of the user and menu attributes; calculating and generating a relation graph between the users according to the relation graph between the users and the menu attributes; and generating a final recommendation candidate list according to the relation graph between the users and the collection behaviors of the target similar users. According to the method, layer-by-layer recommendation list screening is carried out based on the multiple knowledge graphs, and accurate personalized recommendation is realized.

Description

technical field [0001] The invention belongs to the technical field of diet intelligent recommendation, and in particular relates to a method and system for personalized diet recommendation based on a knowledge map. Background technique [0002] With the advent of the information age, information technology has made important progress. How to find the target information of interest in massive data information has become a research hotspot. At present, the mainstream method is mainly the collaborative filtering algorithm based on the user rating mechanism. This algorithm solves the problem of fast similarity search and matching of information to a certain extent, but the algorithm still has some shortcomings, such as cold start and other problems. Food is the most important thing for the people. Chinese recipes have obvious characteristics and a huge number. How to help users obtain the most interesting recipes is obviously a complicated problem. Contents of the invention ...

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Application Information

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IPC IPC(8): G06F16/9535G06F16/36G06F16/2458G06F40/216G06F40/30
CPCG06F16/2465G06F16/367G06F16/9535G06F40/216G06F40/30
Inventor 李宗博陈伯怀杜小军杜乐
Owner 吾征智能技术(北京)有限公司
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