CN113934776A - Food material pushing method, device, medium and equipment - Google Patents
Food material pushing method, device, medium and equipment Download PDFInfo
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Abstract
The application provides a food material pushing method, a food material pushing device, a medium and food material pushing equipment, and relates to the technical field of information extraction. The food material recommending method classifies all recipes in a database according to types of dishes, and determines the rating of all users of a platform on the types of the dishes; acquiring basic data, operation data and scene data; defining basic data as a first behavior, operating data and scene data as a second behavior, and respectively processing the first behavior and the second behavior; determining the similarity between a user x and a user y with the same preference; determining similar user class X, and selecting the first M recipe recommendations with the highest user score in X, wherein M is more than or equal to 10; and recommending corresponding food materials according to the recommended recipes. The method comprises the steps of grading all recipes by using the used grading data, correlating originally abstract data through similarity, constructing a user portrait, matching a user model, perfecting a recipe recommendation method, generating a food material purchasing list according to recommended recipes, and conforming to personal preference of users.
Description
Technical Field
The application relates to the technical field of information extraction, in particular to a food material pushing method, device, medium and equipment.
Background
At present, some intelligent refrigerators in the market can recommend related recipes according to the identified food materials by performing image recognition on the food materials in the refrigerator, so that a user can cook the existing food materials, and the use of the user is facilitated.
At present, some intelligent refrigerators in the market can identify the storage condition of food materials in the refrigerators through images, and then recommend and recommend related recipes according to the identified food materials, so that a user can cook with the existing ingredients, and the intelligent refrigerators are convenient for the user to use. However, in reality, food materials are often lost in the refrigerator and an effective recipe cannot be provided.
Disclosure of Invention
An object of the embodiment of the application is to provide a food material pushing method, device, medium and equipment, aiming at recommending recipes and a food material purchasing list corresponding to the recipes to a client for obtaining the preference of a user.
In order to achieve the above object, embodiments of the present application are implemented as follows:
in a first aspect, an embodiment of the present application provides a food material pushing method, including:
classifying all recipes in the database according to the types of dishes, and determining the rating of all users of the platform on the types of the dishes;
acquiring the name, sex, age, occupation, work and rest time of a user, whether menstruation exists or not and corresponding menstruation, and taking the menstruation as basic data;
acquiring dish preference, recipe collection records and recipe adoption records of a user as operation data;
acquiring the time of starting the recipe by a user, the frequency of starting the recipe for breakfast, lunch and dinner, and the dish type of the recipe for breakfast, lunch and dinner as scene data;
defining basic data as a first behavior, operating data and scene data as a second behavior, respectively processing the first behavior and the second behavior, converting the first behavior into a digital vector, and converting the second behavior into a one-dimensional vector;
determining the similarity between a user x and a user y with the same preference;
determining a similar user class X based on the similarity between the user X and the user y, and selecting the top M recipe recommendations with the highest user score in the X, wherein M is more than or equal to 10;
and recommending corresponding food materials according to the recommended recipes.
With reference to the first aspect, in a first possible implementation manner of the first aspect, recommending a corresponding food material according to a recommended recipe specifically includes the following steps:
acquiring the variety, weight and remaining shelf life of food materials stored by a user;
and pushing a user food material purchasing list and a corresponding recipe according to the stored food materials and the recommended recipe.
With reference to the first aspect, in a second possible implementation manner of the first aspect, recommending a corresponding food material according to a recommended recipe specifically includes the following steps:
acquiring the variety, weight and remaining shelf life of food materials stored by a user;
acquiring a recipe using record of a user within 3 days, shielding the recipe used within 3 days, and not performing recommended display;
and pushing a user food material purchasing list and a corresponding recipe according to the stored food materials, the recommended recipes and the shielded recipes.
With reference to the first aspect, in a third possible implementation manner of the first aspect, recommending a corresponding food material according to a recommended recipe specifically includes the following steps:
recommending corresponding food materials according to the recommended recipes, which specifically comprises the following steps:
acquiring the variety, weight and remaining shelf life of food materials stored by a user;
acquiring a recipe using record of a user within 3 days, shielding the recipe used within 3 days, and not performing recommended display;
and obtaining the current time of the user and the time of the menu to be started, preferentially recommending the menu according to the stored food materials if the time difference between the current time of the user and the time of the menu to be started is not enough for self-purchasing or self-distribution, and pushing a food material purchasing list and a corresponding menu of the user according to the stored food materials, the recommended menu and the shielded menu if the time difference between the current time of the user and the time of the menu to be started is enough for self-purchasing or self-distribution.
With reference to the first aspect, in a third possible implementation manner of the first aspect, the dish types are classified into dish types, dish lines, making difficulty, making time, and dish tastes.
In a second aspect, an embodiment of the present application provides an edible material pushing device, including:
the classification unit is used for classifying all recipes in the database according to the types of the dishes and determining the rating of all users of the platform on the types of the dishes;
the basic unit is used for acquiring the name, the sex, the age, the occupation, the work and rest time of the user, whether menstruation exists or not and the corresponding menstruation period, and the obtained data are used as basic data;
the operation unit is used for acquiring the dish preference, the recipe collection record and the recipe adoption record of a user as operation data;
the scene unit is used for acquiring the time of enabling the recipes by the user, the frequency of enabling the recipes by breakfast, lunch and dinner and the dish types of enabling the recipes by breakfast, lunch and dinner as scene data;
the processing unit is used for defining the basic data as a first behavior, the operation data and the scene data as a second behavior, respectively processing the first behavior and the second behavior, converting the first behavior into a digital vector, and converting the second behavior into a one-dimensional vector;
a similarity unit for determining a similarity between a user x and a user y having the same preference therewith;
the recipe recommending unit is used for determining similar user types X based on the similarity between the users X and y and recommending the top M recipes with the highest user scores in the X, wherein M is more than or equal to 10;
and the food material recommending unit is used for recommending corresponding food materials according to the recommended recipes.
With reference to the second aspect, in a first possible implementation manner of the second aspect, recommending a corresponding food material according to a recommended recipe specifically includes the following steps:
acquiring the variety, weight and remaining shelf life of food materials stored by a user;
and pushing a user food material purchasing list and a corresponding recipe according to the stored food materials and the recommended recipe.
With reference to the second aspect, in a second possible implementation manner of the second aspect, recommending a corresponding food material according to a recommended recipe specifically includes the following steps:
acquiring the variety, weight and remaining shelf life of food materials stored by a user;
acquiring a recipe using record of a user within 3 days, shielding the recipe used within 3 days, and not performing recommended display;
and pushing a user food material purchasing list and a corresponding recipe according to the stored food materials, the recommended recipes and the shielded recipes.
With reference to the second aspect, in a third possible implementation manner of the second aspect, recommending a corresponding food material according to a recommended recipe specifically includes the following steps:
recommending corresponding food materials according to the recommended recipes, which specifically comprises the following steps:
acquiring the variety, weight and remaining shelf life of food materials stored by a user;
acquiring a recipe using record of a user within 3 days, shielding the recipe used within 3 days, and not performing recommended display;
and obtaining the current time of the user and the time of the menu to be started, preferentially recommending the menu according to the stored food materials if the time difference between the current time of the user and the time of the menu to be started is not enough for self-purchasing or self-distribution, and pushing a food material purchasing list and a corresponding menu of the user according to the stored food materials, the recommended menu and the shielded menu if the time difference between the current time of the user and the time of the menu to be started is enough for self-purchasing or self-distribution.
In a third aspect, an embodiment of the present application provides a storage medium, where the storage medium includes a stored program, and when the program runs, a device where the storage medium is located is controlled to execute the food material pushing method according to the first aspect or any one of possible implementation manners of the first aspect.
In a fourth aspect, an embodiment of the present application provides an electronic device, including a memory and a processor, where the memory is configured to store information including program instructions, and the processor is configured to control execution of the program instructions, and the program instructions are loaded and executed by the processor to implement the food material pushing method according to the first aspect or any one of the possible implementation manners of the first aspect.
The application provides a food material recommendation method which classifies all recipes in a database according to dish types and determines the ratings of all users of a platform on the dish types; acquiring basic data, operation data and scene data; defining basic data as a first behavior, operating data and scene data as a second behavior, respectively processing the first behavior and the second behavior, converting the first behavior into a digital vector, and converting the second behavior into a one-dimensional vector; determining the similarity between a user x and a user y with the same preference; determining a similar user class X based on the similarity between the user X and the user y, and selecting the top M recipes with the highest user scores in the X for recommendation; and recommending corresponding food materials according to the recommended recipes. The method comprises the steps of grading all recipes by using used grading data, associating originally abstract data by using similarity through acquisition and use of basic data, operation data and scene data, constructing a user portrait, matching a user model, perfecting a recipe recommendation method, generating a food material purchasing list according to recommended recipes, and conforming to personal preference of users.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a flowchart of a food material pushing method according to an embodiment of the present application.
Fig. 2 is a schematic view of an electronic device according to an embodiment of the present application.
Icon: 20-an electronic device; 21-a memory; 22-a communication module; 23-a bus; 24-a processor.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
Referring to fig. 1, fig. 1 is a flowchart of a food material pushing method according to an embodiment of the present application. In this embodiment, the food material pushing method may be executed by an electronic device, and the electronic device may be a server (e.g., a cloud server, a server cluster, etc.) or a terminal (e.g., a personal computer, a notebook computer, etc.), which is not limited herein.
In the present embodiment, the food material pushing method may include step S10, step S20, step S30, step S40, step S50, step S60, step S70, and step S80.
To push the food material, the electronic device may perform step S10.
Step S10: and classifying all recipes in the database according to the types of the dishes, and determining the rating of all users of the platform on the types of the dishes.
In practice, there are hundreds to thousands of recipes all in the database, when there are few users in the platform, there are recipes that are not rated, and a user has not done a rating operation (e.g., a new user). This affects the accuracy of subsequent recipe pushes.
The calculation formula for determining the rating of all the users of the platform on the dish types is as follows:
wherein f represents the dish type, j represents the jth rated recipe, h represents the h user, and W represents the rating data.
And filling the obtained rating of the dish type into a menu corresponding to the rating of the unknown user, so that each user has a rating for each menu, and the recommendation accuracy can be greatly improved.
Here, the dish types are classified into dish types, dish lines, making difficulty, making time, and dish tastes.
After all recipes in the database are sorted by dish type and the rating of the dish type by all users of the platform is determined, the electronic device may perform step S20.
Step S20: the name, sex, age, occupation, work and rest time of the user and whether menstruation exists or not and the corresponding menstruation period are obtained as basic data.
Here, the basic data of the user may be collected online through an app or a web page.
After acquiring the name, sex, age, occupation, work and rest time of the user and whether there is menstruation and corresponding menstruation period, the electronic device may execute step S30.
Step S30: and acquiring the dish preference, the recipe collection record and the recipe adoption record of the user as operation data.
Where the recipe taken record represents that the user took the recipe and completed cooking.
After obtaining the dish preference, the recipe collection record, and the recipe taking record of the user as the operation data, the electronic device may execute step S40.
Step S40: and acquiring the time of starting the recipe by the user, the frequency of starting the recipe for breakfast, lunch and dinner, and the dish type of the recipe for breakfast, lunch and dinner as scene data.
After acquiring the time when the user starts the recipe, the frequency when the recipe is started for breakfast, lunch, and dinner, and the dish type of the recipe for breakfast, lunch, and dinner as the scene data, the electronic device may perform step S50.
Step S50: defining basic data as a first behavior, operating data and scene data as a second behavior, respectively processing the first behavior and the second behavior, converting the first behavior into a digital vector, and converting the second behavior into a one-dimensional vector.
Assuming that the basic data of the user has n independent attributes, and the user x and the user y exist in the database, converting the basic data of the user in the database into a digital vector in advance according to semantic information, thereby completing the processing of the first behavior; the second behavior is processed by respectively designating the one-dimensional vectors as Situationx = (S)x,1,Sx,2,…,Sx,n)、Situationy=(Sy,1,Sy,2,…,Sy,n) Sim (simple) represents the similarity in both cases. Situationx can be used for the rating of the recipe by the target user when the similarity is larger, which represents the more similar situation.
After defining the basic data as a first behavior and the operation data and the scene data as a second behavior, processing the first behavior and the second behavior respectively, converting the first behavior into a digital vector, and converting the second behavior into a one-dimensional vector, the electronic device may perform step S60.
Step S60: a similarity between user x and user y having the same preference is determined.
Here, in order to acquire the similarity, the following assumption is made:
given a bit vector of user x as (x)1, x2 ,…, xn) The bit vector of user y is (y)1,y2 ,…, yn)，
Assuming that M users and N recipes are provided, establishing a user rating matrix:
wherein the content of the first and second substances,
representing the nth rating of the mth user.
Wherein the content of the first and second substances,
representing a commonly rated menu of users x and y,
represents the average of all ratings of user x,
representing the average of all ratings of user y.
After determining the similarity between the user x and the user y having the same preference, the electronic device may perform step S70.
Step S70: based on the similarity between the user X and the user y, determining a similar user class X, and selecting the top M recipe recommendations with the highest user score in the X, wherein M is more than or equal to 10.
Based on the similarity between the user X and the user y, the similar user class X is determined, and after the top M recipes recommendations with the highest user score in X are taken, the electronic device may execute step S80.
Step S80: and recommending corresponding food materials according to the recommended recipes.
This step can be configured in a variety of logic as desired. For example:
setting 1: acquiring the variety, weight and remaining shelf life of food materials stored by a user;
and pushing a user food material purchasing list and a corresponding recipe according to the stored food materials and the recommended recipe.
Setting 2: acquiring the variety, weight and remaining shelf life of food materials stored by a user;
acquiring a recipe using record of a user within 3 days, shielding the recipe used within 3 days, and not performing recommended display;
and pushing a user food material purchasing list and a corresponding recipe according to the stored food materials, the recommended recipes and the shielded recipes.
Setting 3: recommending corresponding food materials according to the recommended recipes, which specifically comprises the following steps:
acquiring the variety, weight and remaining shelf life of food materials stored by a user;
acquiring a recipe using record of a user within 3 days, shielding the recipe used within 3 days, and not performing recommended display;
and obtaining the current time of the user and the time of the menu to be started, preferentially recommending the menu according to the stored food materials if the time difference between the current time of the user and the time of the menu to be started is not enough for self-purchasing or self-distribution, and pushing a food material purchasing list and a corresponding menu of the user according to the stored food materials, the recommended menu and the shielded menu if the time difference between the current time of the user and the time of the menu to be started is enough for self-purchasing or self-distribution.
Of course, many factors can be referred to for the specific setting of recommending the corresponding food material according to the recommended recipe. Such as the current air temperature, whether the user is dealing with a fat reduction moulding session, whether the user is in the menstrual period, etc. By considering these factors, the classification of the dishes of the recipe may be refined.
In the embodiment of the application, the food material recommendation method classifies all recipes in the database according to the types of dishes, and determines the rating of all users of the platform on the types of the dishes; acquiring basic data, operation data and scene data; defining basic data as a first behavior, operating data and scene data as a second behavior, respectively processing the first behavior and the second behavior, converting the first behavior into a digital vector, and converting the second behavior into a one-dimensional vector; determining the similarity between a user x and a user y with the same preference; determining a similar user class X based on the similarity between the user X and the user y, and selecting the top M recipes with the highest user scores in the X for recommendation; and recommending corresponding food materials according to the recommended recipes. The method comprises the steps of grading all recipes by using used grading data, associating originally abstract data by using similarity through acquisition and use of basic data, operation data and scene data, constructing a user portrait, matching a user model, perfecting a recipe recommendation method, generating a food material purchasing list according to recommended recipes, and conforming to personal preference of users.
Referring to fig. 2, fig. 2 is a schematic view of a food material pushing device according to an embodiment of the present application.
In this embodiment, the food material pushing device may include:
the classification unit is used for classifying all recipes in the database according to the types of the dishes and determining the rating of all users of the platform on the types of the dishes; the basic unit is used for acquiring the name, the sex, the age, the occupation, the work and rest time of the user, whether menstruation exists or not and the corresponding menstruation period, and the obtained data are used as basic data; the operation unit is used for acquiring the dish preference, the recipe collection record and the recipe adoption record of a user as operation data; the scene unit is used for acquiring the time of enabling the recipes by the user, the frequency of enabling the recipes by breakfast, lunch and dinner and the dish types of enabling the recipes by breakfast, lunch and dinner as scene data; the processing unit is used for defining the basic data as a first behavior, the operation data and the scene data as a second behavior, respectively processing the first behavior and the second behavior, converting the first behavior into a digital vector, and converting the second behavior into a one-dimensional vector; a similarity unit for determining a similarity between a user x and a user y having the same preference therewith; the recommendation recipe unit is used for determining similar user types X based on the similarity between the users X and y and recommending the top M recipes with the highest user scores in the X; and the food material recommending unit is used for recommending corresponding food materials according to the recommended recipes.
The food material recommending unit can set various logics according to needs. For example:
setting 1: acquiring the variety, weight and remaining shelf life of food materials stored by a user;
and pushing a user food material purchasing list and a corresponding recipe according to the stored food materials and the recommended recipe.
Setting 2: acquiring the variety, weight and remaining shelf life of food materials stored by a user;
acquiring a recipe using record of a user within 3 days, shielding the recipe used within 3 days, and not performing recommended display;
and pushing a user food material purchasing list and a corresponding recipe according to the stored food materials, the recommended recipes and the shielded recipes.
Setting 3: recommending corresponding food materials according to the recommended recipes, which specifically comprises the following steps:
acquiring the variety, weight and remaining shelf life of food materials stored by a user;
acquiring a recipe using record of a user within 3 days, shielding the recipe used within 3 days, and not performing recommended display;
and obtaining the current time of the user and the time of the menu to be started, preferentially recommending the menu according to the stored food materials if the time difference between the current time of the user and the time of the menu to be started is not enough for self-purchasing or self-distribution, and pushing a food material purchasing list and a corresponding menu of the user according to the stored food materials, the recommended menu and the shielded menu if the time difference between the current time of the user and the time of the menu to be started is enough for self-purchasing or self-distribution.
The embodiment of the application provides a storage medium, where the storage medium includes a stored program, and when the program runs, a device in which the storage medium is located is controlled to execute the food material pushing method in the embodiment.
In addition, referring to fig. 2, fig. 2 is a schematic view of an electronic device 20 according to an embodiment of the present disclosure.
In this embodiment, the electronic device 20 may be a terminal, such as a tablet computer, a personal computer, etc.; the electronic device 20 may also be a server, such as a cloud server, a server cluster, etc., and is not limited herein.
Illustratively, the electronic device 20 may include: a communication module 22 connected to the outside world via a network, one or more processors 24 for executing program instructions, a bus 23, and a different form of memory 21, such as a disk, ROM, or RAM, or any combination thereof. The memory 21, the communication module 22, and the processor 24 may be connected by a bus 23.
Illustratively, the memory 21 has stored therein a program. The processor 24 may call and run the programs from the memory 21, so that the food material pushing method may be implemented by running the programs.
To sum up, the embodiment of the present application provides a food material pushing method, device, medium, and apparatus, in which the food material recommendation method classifies all recipes in a database according to types of dishes, and determines ratings of all users of a platform on the types of the dishes; acquiring basic data, operation data and scene data; defining basic data as a first behavior, operating data and scene data as a second behavior, respectively processing the first behavior and the second behavior, converting the first behavior into a digital vector, and converting the second behavior into a one-dimensional vector; determining the similarity between a user x and a user y with the same preference; determining a similar user class X based on the similarity between the user X and the user y, and selecting the top M recipes with the highest user scores in the X for recommendation; and recommending corresponding food materials according to the recommended recipes. The method comprises the steps of grading all recipes by using used grading data, associating originally abstract data by using similarity through acquisition and use of basic data, operation data and scene data, constructing a user portrait, matching a user model, perfecting a recipe recommendation method, generating a food material purchasing list according to recommended recipes, and conforming to personal preference of users.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A food material pushing method is characterized by comprising the following steps:
classifying all recipes in the database according to the types of dishes, and determining the rating of all users of the platform on the types of the dishes;
acquiring the name, sex, age, occupation, work and rest time of a user, whether menstruation exists or not and corresponding menstruation, and taking the menstruation as basic data;
acquiring dish preference, recipe collection records and recipe adoption records of a user as operation data;
acquiring the time of starting the recipe by a user, the frequency of starting the recipe for breakfast, lunch and dinner, and the dish type of the recipe for breakfast, lunch and dinner as scene data;
defining basic data as a first behavior, operating data and scene data as a second behavior, respectively processing the first behavior and the second behavior, converting the first behavior into a digital vector, and converting the second behavior into a one-dimensional vector;
determining the similarity between a user x and a user y with the same preference;
determining a similar user class X based on the similarity between the user X and the user y, and selecting the top M recipe recommendations with the highest user score in the X, wherein M is more than or equal to 10;
and recommending corresponding food materials according to the recommended recipes.
2. The food material pushing method according to claim 1, wherein the method for recommending the corresponding food material according to the recommended recipe specifically comprises the following steps:
acquiring the variety, weight and remaining shelf life of food materials stored by a user;
and pushing a user food material purchasing list and a corresponding recipe according to the stored food materials and the recommended recipe.
3. The food material pushing method according to claim 1, wherein the method for recommending the corresponding food material according to the recommended recipe specifically comprises the following steps:
acquiring the variety, weight and remaining shelf life of food materials stored by a user;
acquiring a recipe using record of a user within 3 days, shielding the recipe used within 3 days, and not performing recommended display;
and pushing a user food material purchasing list and a corresponding recipe according to the stored food materials, the recommended recipes and the shielded recipes.
4. The food material pushing method according to claim 1, wherein the method for recommending the corresponding food material according to the recommended recipe specifically comprises the following steps:
recommending corresponding food materials according to the recommended recipes, which specifically comprises the following steps:
acquiring the variety, weight and remaining shelf life of food materials stored by a user;
acquiring a recipe using record of a user within 3 days, shielding the recipe used within 3 days, and not performing recommended display;
and obtaining the current time of the user and the time of the menu to be started, preferentially recommending the menu according to the stored food materials if the time difference between the current time of the user and the time of the menu to be started is not enough for self-purchasing or self-distribution, and pushing a food material purchasing list and a corresponding menu of the user according to the stored food materials, the recommended menu and the shielded menu if the time difference between the current time of the user and the time of the menu to be started is enough for self-purchasing or self-distribution.
5. The food material pushing method as claimed in claim 1, wherein the dish type is classified into dish type, dish line, difficulty of making, time of making, and dish taste.
6. A food material pushing device is characterized by comprising
The classification unit is used for classifying all recipes in the database according to the types of the dishes and determining the rating of all users of the platform on the types of the dishes;
the basic unit is used for acquiring the name, the sex, the age, the occupation, the work and rest time of the user, whether menstruation exists or not and the corresponding menstruation period, and the obtained data are used as basic data;
the operation unit is used for acquiring the dish preference, the recipe collection record and the recipe adoption record of a user as operation data;
the scene unit is used for acquiring the time of enabling the recipes by the user, the frequency of enabling the recipes by breakfast, lunch and dinner and the dish types of enabling the recipes by breakfast, lunch and dinner as scene data;
the processing unit is used for defining the basic data as a first behavior, the operation data and the scene data as a second behavior, respectively processing the first behavior and the second behavior, converting the first behavior into a digital vector, and converting the second behavior into a one-dimensional vector;
a similarity unit for determining a similarity between a user x and a user y having the same preference therewith;
the recipe recommending unit is used for determining similar user types X based on the similarity between the users X and y and recommending the top M recipes with the highest user scores in the X, wherein M is more than or equal to 10;
and the food material recommending unit is used for recommending corresponding food materials according to the recommended recipes.
7. The food material pushing device of claim 6, wherein the food material recommending unit is specifically configured to:
acquiring the variety, weight and remaining shelf life of food materials stored by a user;
and pushing a user food material purchasing list and a corresponding recipe according to the stored food materials and the recommended recipe.
8. The food material pushing device of claim 6, wherein the food material recommending unit is specifically configured to:
acquiring the variety, weight and remaining shelf life of food materials stored by a user;
acquiring a recipe using record of a user within 3 days, shielding the recipe used within 3 days, and not performing recommended display;
and pushing a user food material purchasing list and a corresponding recipe according to the stored food materials, the recommended recipes and the shielded recipes.
9. A storage medium, characterized in that the storage medium comprises a stored program, wherein when the program runs, a device where the storage medium is located is controlled to execute the food material pushing method according to any one of claims 1 to 5.
10. An electronic device, characterized by comprising a memory for storing information comprising program instructions and a processor for controlling the execution of the program instructions, wherein the program instructions are loaded and executed by the processor to implement the food material pushing method according to any of claims 1 to 5.
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