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A restaurant ordering recommendation method and system based on multi-user information fusion and entropy

A recommendation method and multi-user technology, applied in digital data information retrieval, data processing applications, character and pattern recognition, etc., to achieve the effect of improving accuracy, improving accuracy and user experience, and eliminating waste

Active Publication Date: 2022-08-09
SHANDONG UNIV +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In order to solve the technical problems in the background technology above, the present invention provides a restaurant ordering recommendation method and system based on multi-user information fusion and entropy, which can output an appropriate amount of personalized menus for users through the recommendation model, and can not only make up for There are deficiencies in the recommendation algorithm, and it can also avoid serious food waste to a large extent, filling the gap in the application of mixed recommendation algorithms in multi-person dining scenarios

Method used

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  • A restaurant ordering recommendation method and system based on multi-user information fusion and entropy
  • A restaurant ordering recommendation method and system based on multi-user information fusion and entropy
  • A restaurant ordering recommendation method and system based on multi-user information fusion and entropy

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Embodiment 1

[0049] like figure 1 As shown, this embodiment provides a restaurant ordering recommendation method based on multi-user information fusion and entropy, including:

[0050] S101: Obtain historical ordering data and weight information, generate a weighted frequent pattern tree, and determine an association rule between dishes and dishes, the number of dishes and the number of diners; the weight information is a weight value that carries the number of diners.

[0051] Among them, determining the association rules between the dishes and the dishes can update the recommendation results in real time and improve the accuracy of the recommendation; determining the association rules between the number of dishes and the number of diners can provide users with different sizes of meals. Control waste.

[0052] This embodiment generates a weighted frequent pattern tree WFP-Tree (Weighted Frequent Pattern-tree) based on the WFP-growth (Weighted Frequent Pattern-growth) algorithm, and then ...

Embodiment 2

[0126] This embodiment provides a restaurant ordering recommendation system based on multi-user information fusion and entropy, which includes:

[0127] A weighted frequent pattern tree generation module, which is used to obtain historical ordering data and weight information, generate a weighted frequent pattern tree, and obtain association rules between dishes and dishes, dishes and the number of diners; the weight information is the number of people carrying diners weight value;

[0128] The candidate subset calculation module is used to calculate the candidate subset of recommended dishes to each user under the same table number based on the weighted frequent pattern tree, entropy and the recommendation matrix of similarity; wherein, each recommended dish carries a gain value information;

[0129] The multi-user information fusion module is used to merge all the candidate subsets under the same table number, add the gain values ​​of the overlapping dishes, sort the dishes...

Embodiment 3

[0132] This embodiment provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, implements the method for restaurant ordering recommendation based on multi-user information fusion and entropy described in the first embodiment above A step of.

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Abstract

The invention provides a restaurant ordering recommendation method and system based on multi-user information fusion and entropy. Among them, the method includes acquiring historical order data and weight information, generating a weighted frequent pattern tree, determining the association rules between dishes and dishes, the number of dishes and the number of diners; receiving the dish preferences of each user under the same table number information, based on the recommendation matrix of weighted frequent pattern tree, entropy and similarity, calculate the candidate subset of recommended dishes to each user under the same table number; Tolerance matrix, merge all candidate subsets under the same table number, add the gain values ​​of overlapping dishes, sort dishes according to the gain value from large to small, and then remove the incompatibility exceeding the preset threshold according to the user incompatibility matrix The number of dishes with the highest gain value and matching the current number of users is selected as the final recommended dishes set, and recommended to all users under the same table number.

Description

technical field [0001] The invention belongs to the technical field of machine learning and information processing, and in particular relates to a restaurant ordering recommendation method and system based on multi-user information fusion and entropy. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] With the development of information technology and the continuous improvement of recommendation algorithms, rich personalized recommendation solutions have penetrated into all fields of people's production and life. The current dish recommendation algorithms used in restaurants are all based on the recommendation of single-person dining, but in actual dining scenarios, most of the people dining at the same table are at least two people, and the recommendation scheme based on single-person dining is obviously not practical. [0004] Traditional ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/9535G06F16/901G06K9/62G06Q50/12
CPCG06F16/9535G06F16/9027G06Q50/12G06F18/2321G06F18/22
Inventor 陈桂友尹梦鑫席斌
Owner SHANDONG UNIV