KR102607701B1 - A method and a apparatus for generating a questionnaire list for building a user food profile - Google Patents

Abstract

The present disclosure relates to a method and device for generating a survey list for building a user food profile. A method according to an embodiment of the present disclosure generates a plurality of first vectors corresponding to one or more ingredients and one or more recipes based on ingredient information and recipe information for a plurality of foods, and generates a plurality of first vectors corresponding to one or more ingredients and one or more recipes. A plurality of second vectors each of which has a degree of similarity greater than a preset value may be derived, and a survey list including foods corresponding to each of the plurality of second vectors may be generated.

Description

Method and apparatus for generating a questionnaire list for building a user food profile {A method and a apparatus for generating a questionnaire list for building a user food profile}

The present invention relates to a method and device for generating a survey list for building a user food profile.

Recently, modern people are introduced to food-related information through various communication media programs and social networking services, and are selecting meal menus based on this information. However, the food information provided is often useless due to differences in individual food preferences, preferences, and tastes.

Under the above-described background, applications that can increase user satisfaction by recommending foods suitable for the user have been developed and are being used. However, these applications either recommend randomly selected foods to the user without considering the user's preferences, or simply recommend to the user foods that are highly preferred by many people. Even when the application recommends food, it recommends a wide range of foods. However, these recommendation results cannot provide high satisfaction to the user and may actually increase the difficulty of menu selection for the user.

The above-mentioned background technology is technical information that the inventor possessed for deriving the present invention or acquired in the process of deriving the present invention, and cannot necessarily be said to be known art disclosed to the general public before filing the application for the present invention.

Domestic Patent Publication No. 10-2022-0026214 (2022.03.04)

The present invention provides a method and device for generating a survey list for building a user food profile. Additionally, the object is to provide a computer-readable recording medium on which a program for executing the method on a computer is recorded.

The problem to be solved by the present invention is not limited to the problems mentioned above, and other problems and advantages of the present invention that are not mentioned can be understood through the following description and can be understood more clearly through the examples of the present invention. It will be. In addition, it can be seen that the problems and advantages to be solved by the present invention can be realized by the means and combinations thereof indicated in the patent claims.

As a technical means for achieving the above-mentioned technical problem, an embodiment of the present disclosure provides, based on ingredient information and recipe information for a plurality of foods, a plurality of dishes corresponding to at least one ingredient and at least one recipe. 1 generating vectors; Deriving a plurality of second vectors whose similarity to each of the plurality of first vectors is equal to or greater than a preset value; and generating a survey list including foods corresponding to each of the plurality of second vectors. A method of generating a survey list for building a user food profile can be provided.

Generating the plurality of first vectors may include generating a first tree regarding ingredients and a second tree regarding recipes based on ingredient information and recipe information regarding the plurality of foods; and generating the plurality of first vectors based on the first tree and the second tree.

The first tree may include nodes set to ingredient categories for each of the plurality of foods, and the second tree may include nodes set to recipe categories for each of the plurality of foods.

Each of the plurality of first vectors may correspond to at least one node included in the first tree and at least one node included in the second tree.

Each of the plurality of first vectors corresponds to a first node that is at least one terminal node included in the first tree and a second node that is at least one terminal node included in the second tree. It may be.

At least one of the plurality of first vectors corresponds to a third node that is an ancestor node of the first node and a fourth node that is an ancestor node of the second node, and the third node and the fourth node The node may be other than the root node.

Each of the plurality of second vectors may be generated based on ingredient information and recipe information for one food.

Each of the plurality of second vectors uses a third tree related to ingredients generated based on ingredient information about one food and a fourth tree related to recipes generated based on recipe information about one food. It may have been created by doing so.

The method may further include generating an additional survey list based on response information corresponding to the survey list.

The step of generating the additional survey list includes generating a fifth tree regarding preferred ingredients and a sixth tree regarding preferred recipes based on response information corresponding to the survey list; Based on the fifth tree and the sixth tree, generating a plurality of third vectors corresponding to at least one food ingredient and at least one recipe; Deriving a plurality of fourth vectors whose similarity to each of the plurality of third vectors is greater than or equal to a preset value; and generating an additional survey list including foods corresponding to each of the plurality of fourth vectors.

Another embodiment of the present disclosure includes at least one memory; and at least one processor, wherein the at least one processor generates a plurality of first vectors corresponding to at least one ingredient and at least one recipe based on ingredient information and recipe information regarding the plurality of foods. generating, deriving a plurality of second vectors whose similarity with each of the plurality of first vectors is greater than or equal to a preset value, and generating a survey list including foods corresponding to each of the plurality of second vectors. , computing devices can be provided.

Another embodiment of the present disclosure may provide a computer-readable recording medium that records a program for executing the method according to the embodiment on a computer.

In addition, another method for implementing the present invention, another system, and a computer-readable recording medium storing a computer program for executing the method may be further provided.

Other aspects, features and advantages in addition to those described above will become apparent from the following drawings, claims and detailed description of the invention.

According to the means for solving the problem of the present disclosure described above, in the present disclosure, a plurality of first vectors corresponding to one or more ingredients and one or more recipes are generated based on ingredient information and recipe information for a plurality of foods, and a plurality of first vectors corresponding to one or more ingredients and one or more recipes are generated. By deriving a plurality of second vectors whose similarity to each of the first vectors is more than a preset value and generating a survey list including foods corresponding to each of the plurality of second vectors, users can more accurately identify all food categories You can understand your preferences.

In addition, according to the problem solving means of the present disclosure, it is possible to prevent the same user from being repeatedly surveyed about foods belonging to similar categories.

In addition, according to the problem solving means of the present disclosure, it is possible to prevent the interpretation of the questionnaire from becoming ambiguous as the user responds with conflicting preferences for foods belonging to some overlapping categories.

The effects of the present invention are not limited to those mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below.

1 is a diagram illustrating a system for generating a survey list for building a user food profile according to an embodiment.
FIG. 2A is a configuration diagram illustrating an example of a user terminal according to an embodiment.
Figure 2b is a configuration diagram illustrating an example of a server according to an embodiment.
Figure 3 is a flowchart illustrating an example of a method for generating a survey list for building a user food profile according to an embodiment.
FIG. 4 is a flowchart illustrating an example in which a processor generates a plurality of first vectors according to an embodiment.
Figure 5 is a diagram for explaining an example of a first tree and a first node related to food ingredients according to an embodiment.
Figure 6 is a diagram for explaining an example of a second tree and a second node related to a recipe according to an embodiment.
FIG. 7 is a diagram for explaining an example of a third node according to an embodiment.
FIG. 8 is a diagram for explaining an example of a fourth node according to an embodiment.
Figure 9 is a flowchart illustrating another example of a method for generating a survey list for building a user food profile according to an embodiment.
FIG. 10 is a flowchart illustrating an example in which a processor generates an additional survey list according to an embodiment.
Figure 11 is a diagram for explaining an example of a fifth tree related to preferred food ingredients according to an embodiment.
Figure 12 is a diagram for explaining a sixth tree related to preferred recipes according to an embodiment.

BRIEF DESCRIPTION OF THE DRAWINGS Various embodiments of the present disclosure are described below in conjunction with the accompanying drawings. Various embodiments of the present disclosure can make various changes and have various embodiments, and specific embodiments are illustrated in the drawings and related detailed descriptions are described. However, this is not intended to limit the various embodiments of the present disclosure to specific embodiments, and should be understood to include all changes and/or equivalents or substitutes included in the spirit and technical scope of the various embodiments of the present disclosure. In connection with the description of the drawings, similar reference numbers have been used for similar components.

“Includes” may be used in various embodiments of the present disclosure. or “may include.” Expressions such as "disclosed" indicate the existence of the corresponding function, operation, or component, and do not limit one or more additional functions, operations, or components. Additionally, in various embodiments of the present disclosure, “including.” Or “to have.” Terms such as are intended to designate the presence of features, numbers, steps, operations, components, parts, or a combination thereof described in the specification, but are intended to indicate the presence of one or more other features, numbers, steps, operations, components, parts, or It should be understood that the existence or addition possibility of combinations of these is not excluded in advance.

In various embodiments of the present disclosure, expressions such as “or” include any and all combinations of words listed together. For example, “A or B” may include A, B, or both A and B.

Expressions such as “first,” “second,” “first,” or “second” used in various embodiments of the present disclosure may modify various elements of the various embodiments, but do not limit the elements. No. For example, the above expressions do not limit the order and/or importance of the corresponding components. The above expressions can be used to distinguish one component from another. For example, the first user device and the second user device are both user devices and represent different user devices. For example, a first component may be referred to as a second component, and similarly, the second component may be referred to as a first component without departing from the scope of various embodiments of the present disclosure.

In embodiments of the present disclosure, terms such as “module”, “unit”, “part”, etc. are terms to refer to components that perform at least one function or operation, and these components are either hardware or software. It may be implemented or may be implemented through a combination of hardware and software. In addition, a plurality of "modules", "units", "parts", etc. are integrated into at least one module or chip, except in cases where each needs to be implemented with individual specific hardware, and is integrated into at least one processor. It can be implemented as:

Terms used in the various embodiments of the present disclosure are merely used to describe specific embodiments and are not intended to limit the various embodiments of the present disclosure. Singular expressions include plural expressions unless the context clearly dictates otherwise.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the technical field to which the various embodiments of the present disclosure pertain.

Terms such as those defined in commonly used dictionaries should be interpreted as having meanings consistent with the meanings they have in the context of the related technology, and unless clearly defined in various embodiments of the present disclosure, are not ideal or excessively formal. It is not interpreted as meaning.

Hereinafter, various embodiments of the present invention will be described in detail using the attached drawings.

1 is a diagram illustrating a system for generating a survey list for building a user food profile according to an embodiment.

Referring to Figure 1, system 1 includes a user terminal 10 and a server 20. For example, the user terminal 10 and the server 20 can be connected by wired or wireless communication to transmit and receive data (e.g., ingredient information about a plurality of foods and recipe information about a plurality of foods) between them. there is.

For convenience of explanation, FIG. 1 shows that the system 1 includes a user terminal 10 and a server 20, but the system 1 is not limited thereto. For example, the system 1 may include at least one external device, and the operations of the user terminal 10 and the server 20, which will be described below, may be performed using a single device (e.g., the user terminal 10 or the server 20). (20)) Alternatively, it may be implemented by multiple devices.

The user terminal 10 may be a computing device that includes a display device and a memory and a processor. For example, a display device may be implemented as a touch screen to receive user input. Alternatively, the user terminal 10 may further include a separate device (eg, keyboard, mouse, etc.) that receives user input. For example, the user terminal 10 may include a smart phone, a laptop PC, a desktop PC, a laptop, a tablet computer, etc., but is not limited thereto.

The server 20 may be a device that communicates with an external device (not shown) including the user terminal 10. As an example, the server 20 may provide ingredient information about a plurality of foods, recipe information about a plurality of foods, a plurality of first vectors corresponding to one or more ingredients and one or more recipes, and a plurality of first vectors. It may be a device that stores various data including a plurality of second vectors whose similarity to each is more than a preset value, foods corresponding to each of the plurality of second vectors, etc.

Alternatively, the server 20 may be a computing device that includes memory and a processor and has its own computing capabilities. When the server 20 is a computing device, the server 20 may perform at least some of the operations of the user terminal 10, which will be described later with reference to FIGS. 1 to 12. For example, the server 20 may be a cloud server, but is not limited thereto.

The user terminal 10 may output an image 40 representing a survey list for building a food profile of the user 30. For example, the user terminal 10 may allow the user 30 to generate a plurality of first vectors corresponding to one or more ingredients and one or more recipes based on ingredient information and recipe information for a plurality of foods. . Additionally, the user terminal 10 may derive a plurality of second vectors whose similarity to each of the plurality of first vectors is equal to or greater than a preset value. Additionally, the user terminal 10 may output a survey list including foods corresponding to each of the plurality of second vectors as an image 40 .

For example, the user terminal 10 may provide a survey list for building a food profile of the user 30 through an application installed on the user terminal 10. Here, the application may be a software program designed for the purpose of catering activities for the user 30. For example, through the application, the user 30 can perform various restaurant activities, such as payment for restaurant products or restaurant services.

Meanwhile, for convenience of explanation, throughout the specification, the user terminal 10 generates a plurality of first vectors corresponding to one or more ingredients and one or more recipes based on ingredient information and recipe information for a plurality of foods. It was described as generating, deriving a plurality of second vectors whose similarity with each of the plurality of first vectors is greater than a preset value, and generating a survey list including foods corresponding to each of the plurality of second vectors. It is not limited to this. For example, at least some of the operations performed by the user terminal 10 may be performed by the server 20.

In other words, at least some of the operations of the user terminal 10 described with reference to FIGS. 1 to 12 may be performed by the server 20. For example, the server 20 may generate a plurality of first vectors corresponding to one or more ingredients and one or more recipes based on ingredient information and recipe information for a plurality of foods. Additionally, the server 20 may derive a plurality of second vectors whose similarity to each of the plurality of first vectors is greater than or equal to a preset value. Additionally, the server 20 may generate a survey list including foods corresponding to each of the plurality of second vectors. However, the operation of the server 20 is not limited to the above.

FIG. 2A is a configuration diagram illustrating an example of a user terminal according to an embodiment.

Referring to FIG. 2A, the user terminal 100 includes a processor 110, a memory 120, an input/output interface 130, and a communication module 140. For convenience of explanation, only components related to the present invention are shown in FIG. 2A. Accordingly, in addition to the components shown in FIG. 2A, other general-purpose components may be further included in the user terminal 100. In addition, the processor 110, memory 120, input/output interface 130, and communication module 140 shown in FIG. 2A may be implemented as independent devices, as will be understood by those skilled in the art related to the present invention. It is self-evident.

The processor 110 can process computer program instructions by performing basic arithmetic, logic, and input/output operations. Here, the command may be provided from the memory 120 or an external device (eg, server 20, etc.). Additionally, the processor 110 may generally control the operations of other components included in the user terminal 100.

The processor 110 generates a plurality of first vectors corresponding to one or more ingredients and one or more recipes based on ingredient information and recipe information for a plurality of foods. For example, the processor 110 generates a first tree about ingredients and a second tree about recipes based on ingredient information and recipe information about a plurality of foods, and based on the first tree and the second tree. Thus, a plurality of first vectors can be generated.

Here, ingredient information about a plurality of foods may refer to information about all ingredients used in cooking each of the plurality of foods. Specifically, ingredient information regarding a plurality of foods may include information regarding the ingredients used in cooking each of the plurality of foods. For example, ingredient information about food in which beef is used may include information about beef or cattle. However, examples of ingredient information regarding a plurality of foods are not limited to the above.

Meanwhile, food ingredients can be divided into relative concepts such as high-level food ingredients and low-level food ingredients, and high-level food ingredients can be classified into low-level food ingredients. For example, “food ingredients” can be classified into sub-ingredients: “land” ingredients and “sea” ingredients. In addition, high-order food ingredients called “terrestrial” ingredients can be classified into low-level ingredients called “animal” ingredients and “plant” ingredients. In addition, high-level food ingredients called “animal” food ingredients can be classified into low-level food ingredients called “mammal” food ingredients and “poultry” food ingredients. And, the high-level food ingredients called “plant” plant food ingredients can be classified into low-level food ingredients called “pepper” food ingredients and “bean” food ingredients. Here, “animal” ingredients correspond to the upper level of “mammal” ingredients, but they also correspond to the lower level of “terrestrial” ingredients. However, examples of food ingredients are not limited to those described above.

Here, recipe information about a plurality of foods may refer to information about all recipes used in cooking each of a plurality of foods. Specifically, recipe information about a plurality of foods may include information about the recipe used to cook each of the plurality of foods. For example, recipe information about food in which baking is used may include information about baking. However, examples of recipe information for multiple foods are not limited to those described above.

Meanwhile, recipes can be divided into relative concepts of upper-level recipes and lower-level recipes, and upper-level recipes can be classified into lower-level recipes. For example, “recipes” can be classified into sub-recipes: “heated” recipes, “non-heated” recipes, and “non-processed” recipes. In addition, the upper level ingredients called “heating” recipes can be classified into lower level recipes called “boiling” recipes, “steaming” recipes, and “baking” recipes. And, the upper level recipe called “non-heating” recipe can be classified into lower level recipe called “fermentation” recipe and “radish” recipe. Here, the “heating” recipe corresponds to the parent recipe of the “boiling” recipe, but also corresponds to the child recipe of “recipe”. However, examples of recipes are not limited to those described above.

Here, the first tree refers to a tree generated based on ingredient information about a plurality of foods. For example, the first tree may include nodes set as ingredients for each of a plurality of foods.

Here, the second tree refers to a tree generated based on recipe information about a plurality of foods. For example, the second tree may include nodes set to recipes for each of a plurality of foods.

Here, the plurality of first vectors are vectors generated based on ingredient information and recipe information for a plurality of foods, and may correspond to one or more ingredients and one or more recipes. As an example, each of the plurality of first vectors may correspond to one or more nodes included in the first tree and one or more nodes included in the second tree. As another example, each of the plurality of first vectors may correspond to a first node that is one or more terminal nodes included in the first tree and a second node that is one or more terminal nodes included in the second tree. However, examples of the plurality of first vectors are not limited to those described above.

Meanwhile, the plurality of first vectors are vectors generated based on ingredient information and recipe information for a plurality of foods, and may correspond to one ingredient and one recipe. As an example, each of the plurality of first vectors may correspond to one node included in the first tree and one node included in the second tree. As another example, each of the plurality of first vectors may correspond to a first node that is one terminal node included in the first tree and a second node that is one terminal node included in the second tree. Since each of the plurality of first vectors corresponds to one food ingredient and one recipe, each of the plurality of generated first vectors may represent different categories of food. However, examples of the plurality of first vectors are not limited to those described above.

Meanwhile, the plurality of first vectors may correspond to one food ingredient and one recipe, and may be generated based on a first tree related to the food ingredient and a second tree related to the recipe. The plurality of first vectors may correspond to virtual food or may correspond to real food. For example, if the first vector corresponds to one “small” ingredient and one “fermentation” recipe, the first vector is an actual vector that uses only one “small” ingredient and one “fermentation” recipe. It may correspond to food. However, if there is no real food that uses only one “small” ingredient and one “fermentation” recipe, the first vector may be a hypothetical food that uses only one “small” ingredient and one “fermentation” recipe. . However, examples of the first vector are not limited to those described above.

Then, the processor 110 derives a plurality of second vectors whose similarity to each of the plurality of first vectors is greater than or equal to a preset value.

Here, the similarity may be cosine similarity, Euclidian distance, Jaccard similarity, Manhattan distance, Minkowski Distance, etc. However, examples of similarity are not limited to those described above.

Here, the preset value may be a value set by the processor 110 or a value set by the user 30. The preset value may be set to a different value for each of the plurality of first vectors. However, examples of preset values are not limited to the above.

Here, the plurality of second vectors may be vectors generated based on ingredient information and recipe information for one food. For example, each of the plurality of second vectors may be vectors generated based on ingredient information and recipe information for each of foods such as curry rice bowl, beef radish soup, and tomato spaghetti. The second vector may be a vector generated based on ingredient information and recipe information for curry rice bowl, or may be a vector generated based on ingredient information and recipe information for beef radish soup. However, examples of the plurality of second vectors are not limited to those described above.

Additionally, the processor 110 may generate a survey list including foods corresponding to each of the plurality of second vectors.

As described above, each of the plurality of first vectors corresponds to one food ingredient and one recipe, so that each of the plurality of first vectors generated may correspond to different categories of food. Additionally, the plurality of second vectors having a similarity with each of the first vectors corresponding to each of the different categories of food may be equal to or greater than a preset value, and may likewise correspond to each of the different categories of foods. According to one embodiment of the present invention, by creating a survey list containing foods of different categories, it is possible to more accurately determine the user's preference for all food categories, and the same user can repeatedly identify foods belonging to similar categories. It is possible to prevent questionnaires from being asked to people, and it is possible to prevent the interpretation of the questionnaire from being ambiguous as users respond with conflicting preferences for foods belonging to some overlapping categories.

Additionally, the processor 110 may generate an additional survey list based on response information corresponding to the survey list. Specifically, the processor 110 may generate a fifth tree regarding preferred ingredients and a sixth tree regarding preferred recipes based on response information corresponding to the survey list. Additionally, the processor 110 may generate a plurality of third vectors corresponding to one or more food ingredients and one or more recipes based on the fifth tree and the sixth tree. Additionally, the processor 110 may derive a plurality of fourth vectors whose similarity to each of the plurality of third vectors is greater than or equal to a preset value. Additionally, the processor 110 may generate an additional survey list including foods corresponding to each of the plurality of fourth vectors.

The processor 110 may be implemented as an array of multiple logic gates, or may be implemented as a combination of a general-purpose microprocessor and a memory storing a program that can be executed on the microprocessor. For example, processor 110 may include a general-purpose processor, central processing unit (CPU), microprocessor, digital signal processor (DSP), controller, microcontroller, state machine, etc. In some circumstances, processor 110 may include an application specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), etc. For example, processor 110 may be a combination of a digital signal processor (DSP) and a microprocessor, a combination of a plurality of microprocessors, a combination of one or more microprocessors combined with a digital signal processor (DSP) core, or any other such. It may also refer to a combination of processing devices, such as a combination of configurations.

Memory 120 may include any non-transitory computer-readable recording medium. As an example, the memory 120 is a non-permanent mass storage device such as random access memory (RAM), read only memory (ROM), disk drive, solid state drive (SSD), flash memory, etc. device). As another example, non-perishable mass storage devices such as ROM, SSD, flash memory, disk drives, etc. may be a separate persistent storage device distinct from memory. Additionally, the memory 210 may store an operating system (OS) and at least one program code (e.g., code for the processor 110 to perform operations to be described later with reference to FIGS. 3 to 12).

These software components may be loaded from a computer-readable recording medium separate from the memory 120. The recording medium readable by such a separate computer may be a recording medium that can be directly connected to the user terminal 100, for example, a floppy drive, disk, tape, DVD/CD-ROM drive, memory card, etc. It may include a readable recording medium. Alternatively, software components may be loaded into the memory 120 through the communication module 140 rather than a computer-readable recording medium. For example, at least one program is a computer program installed by files provided through the communication module 140 by developers or a file distribution system that distributes the installation file of the application (e.g., FIGS. 3 to 12 It may be loaded into the memory 120 based on a computer program (such as a computer program for the processor 110 to perform an operation to be described later with reference to).

The input/output interface 130 may be a means for interfacing with a device for input or output (e.g., touch screen, keyboard, mouse, etc.) that may be connected to or included in the user terminal 100. . In FIG. 2A, the input/output interface 130 is shown as an element configured separately from the processor 110, but the present invention is not limited thereto, and the input/output interface 130 may be included in the processor 110.

The communication module 140 may provide a configuration or function for the server 20 and the user terminal 100 to communicate with each other through a network. Additionally, the communication module 140 may provide a configuration or function for the user terminal 100 to communicate with other external devices. For example, control signals, commands, data, etc. provided under control of the processor 110 may be transmitted to the server 20 and/or an external device through the communication module 140 and the network.

Meanwhile, although not shown in FIG. 2A, the user terminal 100 may further include a display device. For example, the display device may be implemented as a touch screen. Alternatively, the user terminal 100 may be connected to an independent display device through wired or wireless communication to transmit and receive data between them. For example, a survey list, an additional survey list, etc. may be provided through the display device.

Figure 2b is a configuration diagram illustrating an example of a server according to an embodiment.

Referring to FIG. 2B, the server 20 includes a processor 210, a memory 220, and a communication module 230. For convenience of explanation, only components related to the present invention are shown in FIG. 2B. Accordingly, in addition to the components shown in FIG. 2B, other general-purpose components may be further included in the server 200. Additionally, it is obvious to those skilled in the art that the processor 210, memory 220, and communication module 230 shown in FIG. 2B may be implemented as independent devices.

The processor 210 may obtain ingredient information about a plurality of foods and recipe information about a plurality of foods from at least one of the memory 220, the user terminal 10, or an external device. The processor 210 may generate a plurality of first vectors corresponding to one or more ingredients and one or more recipes based on ingredient information and recipe information for a plurality of foods. Additionally, the processor 210 may derive a plurality of second vectors whose similarity to each of the plurality of first vectors is equal to or greater than a preset value. Additionally, the processor 210 may generate a survey list including foods corresponding to each of the plurality of second vectors. Additionally, the processor 210 may generate an additional survey list based on response information corresponding to the survey list.

In other words, at least one of the operations of the processor 110 described above with reference to FIG. 2A may be performed by the processor 210. In this case, the user terminal 100 may output information transmitted from the server 200 through a display device.

Meanwhile, since the implementation example of the processor 210 is the same as the implementation example of the processor 110 described above with reference to FIG. 2A, detailed description is omitted.

The memory 220 may store various data, such as data required for the operation of the processor 210 and data generated according to the operation of the processor 210. Additionally, the memory 220 may store an operating system (OS) and at least one program (eg, a program necessary for the processor 210 to operate, etc.).

Meanwhile, since the implementation example of the memory 220 is the same as the implementation example of the memory 220 described above with reference to FIG. 2A, detailed description will be omitted.

The communication module 230 may provide a configuration or function for the server 200 and the user terminal 100 to communicate with each other through a network. Additionally, the communication module 140 may provide a configuration or function for the server 200 to communicate with other external devices. For example, control signals, commands, data, etc. provided under control of the processor 210 may be transmitted to the user terminal 100 and/or an external device through the communication module 230 and the network.

Figure 3 is a flowchart illustrating an example of a method for generating a survey list for building a user food profile according to an embodiment.

Referring to FIG. 3, the method of generating a survey list for building a user food profile consists of steps processed in time series in the user terminals 10 and 100 or the processor 110 shown in FIGS. 1 and 2A. do. Therefore, even if the content is omitted below, the content described above with respect to the user terminals 10 and 100 or the processor 110 shown in FIGS. 1 and 2A is used to build a user food profile of the user 30 of FIG. 3. It can also be applied to the method of creating a survey list for.

In addition, as described above with reference to FIGS. 1 and 2B, at least one of the steps of the method for generating a survey list for building the user food profile of the user 30 of FIG. 3 is performed by the server 20 or the processor. It can be processed at (210).

In step 310, the processor 110 generates a plurality of first vectors corresponding to one or more ingredients and one or more recipes based on ingredient information and recipe information for a plurality of foods. Specifically, the processor 110 obtains ingredient information and recipe information for each of a plurality of foods. Then, the processor 110 generates a plurality of first vectors corresponding to one or more ingredients and one or more recipes based on the ingredient information and recipe information.

First, the processor 110 obtains ingredient information and recipe information for each of a plurality of foods. The processor 110 may obtain ingredient information and recipe information for each of a plurality of foods from at least one of the memory 120, the server 20, or an external device (not shown). However, the method by which the processor 110 obtains ingredient information and recipe information for each of a plurality of foods is not limited to the above-described example.

Then, the processor 110 generates a plurality of first vectors corresponding to one or more ingredients and one or more recipes based on the ingredient information and recipe information.

Meanwhile, the processor 110 generates a plurality of first vectors corresponding to one ingredient and one recipe based on the ingredient information and recipe information. The first vector may correspond to one food ingredient and one recipe. Accordingly, each of the plurality of first vectors may not correspond to the same food ingredients and the same recipe. Each of the plurality of first vectors may correspond to different categories of food.

Hereinafter, with reference to FIG. 4, an example in which a processor according to an embodiment generates a plurality of first vectors will be described.

FIG. 4 is a flowchart illustrating an example in which a processor generates a plurality of first vectors according to an embodiment.

In step 410, the processor 110 generates a first tree regarding ingredients and a second tree regarding recipes based on ingredient information and recipe information for a plurality of foods.

Hereinafter, with reference to FIGS. 5 and 6, an example of a first tree related to food ingredients and an example of a second tree related to recipes will be described.

Figure 5 is a diagram for explaining an example of a first tree and a first node related to food ingredients according to an embodiment.

Referring to FIG. 5, an example of a first tree and a first node related to food ingredients generated by the processor can be seen.

The processor 110 creates a first tree related to food ingredients, including a root node set as “food ingredients,” child nodes of the root node set as “land” ingredients and “sea” ingredients, which are subordinate ingredients of “food ingredients” ( 500) can be created. Additionally, the processor 110 may generate a first tree 500 regarding food ingredients including nodes set as “animal” ingredients and “plant” ingredients, which are lower level ingredients of “terrestrial” ingredients. In addition, the processor 110 uses “cow” ingredients (510), “pork” ingredients (520), “chicken” ingredients (530), “duck” ingredients (540), “pepper” ingredients (550), and “beans”. A first tree 500 regarding food ingredients can be created including terminal nodes set to food ingredients 560, “fish” ingredients 570, “crustacean” ingredients 580, and “seaweed” ingredients. . As described above, the processor 110 may generate a first tree including nodes set as ingredients that can be classified by relative concepts such as upper-order ingredients and lower-order ingredients. However, the example of the first tree is not limited to the above.

Figure 6 is a diagram for explaining an example of a second tree and a second node related to a recipe according to an embodiment.

The processor 110 includes a root node set to “recipe”, a “heating” recipe that is a child recipe of “recipe”, a “non-heating” recipe, and a child node of the root node set to a “non-processing” recipe. A second tree 600 related to recipes can be created. Additionally, the processor 110 may generate a second tree 600 for recipes including nodes set as “boiling” recipes, “steaming” recipes, and “baking” recipes, which are sub-recipes of the “heating” recipe. In addition, the processor 110 can be used to prepare a “boiling” recipe (610), a “steaming” recipe (620), a “baking” recipe (630), a “fermentation” recipe (640), a “seasoning” recipe (650), and a “bake” recipe (650). A second tree 600 regarding the recipe including terminal nodes set as “processing” recipes 660 can be created. As described above, the processor 110 may generate a second tree including nodes set with recipes that can be classified into relative concepts of upper recipe and lower recipe. However, the example of the second tree is not limited to the above.

Referring again to FIG. 4, in step 420, the processor 110 generates a plurality of first vectors based on the first tree and the second tree.

As an example, the processor 110 may generate a vector based on the first tree and a vector based on the second tree, and generate the first vector using the vector based on the first tree and the vector based on the second tree. .

As another example, processor 110 generates a matrix based on the first tree and a matrix based on the second tree, uses the matrix based on the first tree to generate a vector based on the first tree, and generates a vector based on the second tree. A vector based on the second tree can be generated using a matrix, and a first vector can be generated using a vector based on the first tree and a vector based on the second tree. Here, the matrix based on the first tree may be implemented as a matrix with the ingredients corresponding to the nodes included in the first tree as row and column elements, and the ingredients corresponding to the nodes included in the first tree. The elements of rows and columns may be configured in order from upper level ingredients to lower level ingredients, but are not limited to this. Here, the matrix based on the second tree may be implemented as a matrix with recipes corresponding to nodes included in the second tree as row and column elements, respectively, and recipes corresponding to nodes included in the second tree. The elements of rows and columns may be configured in the order from the upper recipe to the lower recipe, but are not limited to this.

Meanwhile, each of the plurality of first vectors may correspond to a first tree and a second tree.

As an example, each of the plurality of first vectors may correspond to one or more nodes included in the first tree and one or more nodes included in the second tree.

As another example, each of the plurality of first vectors may correspond to one node included in the first tree and one node included in the second tree.

As another example, each of the plurality of first vectors may correspond to a first node that is one or more terminal nodes included in the first tree and a second node that is one or more terminal nodes included in the second tree.

As another example, each of the plurality of first vectors may correspond to a first node that is one terminal node included in the first tree and a second node that is one terminal node included in the second tree.

As another example, at least one of the plurality of first vectors may correspond to a third node that is an ancestor node of the first node and a fourth node that is an ancestor node of the second node. Here, the third node and fourth node may not be the root node. The number of third nodes, which are the ancestor nodes of the first node, may be smaller than that of the first node, and the number of fourth nodes, which are the ancestor nodes of the second node, may be smaller than that of the second node. Accordingly, the processor 110 can easily adjust the number of foods included in the survey list by generating a plurality of first vectors corresponding to the third and fourth nodes, rather than the first and second nodes. .

Hereinafter, with reference to Tables 1 to 3, an example of generating a first vector according to an embodiment will be described.

number One 2 3 4 5 6 7 8 9 ingredients cow pig chicken duck pepper bean fish shellfish seaweed

Referring to Table 1, it can be seen that the terminal nodes 510 to 590 included in the first tree 500 are numbered and organized based on the first tree 500 shown in FIG. 5. It can be seen that there are a total of 9 terminal nodes 510 to 590 in the first tree 500.

number One 2 3 4 5 6 recipe boil steaming roast fermentation Muchigi unprocessed

Referring to Table 2, it can be seen that the terminal nodes 610 to 660 included in the second tree 600 are numbered and organized based on the second tree 600 shown in FIG. 6. It can be seen that there are a total of six terminal nodes (610 to 660) in the second tree 600.

Meanwhile, referring to Table 1 and Table 2 above, it can be seen that there are 9 terminal nodes 510 to 590 related to food ingredients and 6 terminal nodes 610 to 660 related to recipes. As described above, the first vectors may correspond to a first node that is at least one terminal node included in the first tree and a second node that is at least one terminal node included in the second tree. The first vectors according to one embodiment are a total of 54 vectors corresponding to a combination of one of nine terminal nodes (510 to 590) related to food ingredients and one of six terminal nodes (610 to 660) related to recipes. 1 can be vectors.

number One 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Ingredients and Recipes cow pig chicken duck pepper bean fish shellfish seaweed boil steaming roast fermentation Muchigi unprocessed Applicable or not One 0 0 0 0 0 0 0 0 One 0 0 0 0 0

Referring to Table 3, based on the first tree 500 shown in FIG. 5 and the second tree 600 shown in FIG. 6, terminal nodes 510 to 590 included in the first tree 500 It can be seen that the terminal nodes 610 to 660 included in the second tree 600 are numbered and organized. At this time, the first vector may correspond to the “small” ingredients and the “boiling” recipe. The first vector corresponding to the “small” ingredient and the “boiling” recipe may be one of the 54 first vectors described above.

Hereinafter, an example of the first vector will be described with reference to Equations 1 to 3.

Referring to Equation 1, the vector (V _i ) based on the first tree related to food ingredients can be confirmed. A vector based on the first tree related to food ingredients may include values (V _i1 , V _i2 , ..., V _in ) corresponding to the nodes constituting the first tree, in order from the upper food ingredient to the lower food ingredient. Here, n means the number of nodes constituting the first tree related to food ingredients. However, examples of vectors based on the first tree related to food ingredients are not limited to those described above.

Referring to Equation 2, the vector (V _r ) based on the second tree related to the recipe can be confirmed. A vector based on the second tree related to food ingredients may include values (V _r1 , V _r2 , ..., V _rn ) corresponding to the nodes constituting the second tree in order from the upper recipe to the lower recipe. Here, n means the number of nodes constituting the second tree related to the recipe. However, examples of vectors based on the second tree for recipes are not limited to those described above.

Referring to Equation 3, the first vector (V _f ) generated by concatenating a vector (V _i ) based on the first tree regarding food ingredients and a vector (V _r ) based on the second tree regarding recipes is You can check it. As an example, the first vector (V _f ) includes a vector (V _i ) based on a first tree related to food ingredients and a vector (V _r ) based on a second tree related to recipes in order from upper level ingredients to lower level ingredients; , it may be included in the order from top recipe to bottom recipe. As another example, the first vector (V _f ) includes a vector (V _r ) based on the second tree related to the recipe and a vector (V _i ) based on the first tree related to the ingredients in order from the upper recipe to the lower recipe, , it may be included in the order from top food ingredients to bottom food ingredients. However, the example of the first vector is not limited to the above.

Meanwhile, the first vector corresponding to the “small” ingredients and “boiling” recipe according to Table 3 is {1, 0, 0, 0, 0, 0, 0, 0, 0, 1, It can be expressed as 0, 0, 0, 0, 0}. Likewise, the first vector corresponding to the “fish” ingredient and the “fermentation” recipe is {0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, It can be expressed as 1, 0, 0}. The 54 first vectors described above can also be expressed in the same way, but the method is not limited to this.

Hereinafter, with reference to FIGS. 7 and 8, an example of a third node and a fourth node according to an embodiment will be described.

FIG. 7 is a diagram for explaining an example of a third node according to an embodiment.

The tree 700 of FIG. 7 corresponds to the first tree 500 of FIG. 5, and redundant description will be omitted.

According to the above description, the processor 110 may generate the first vector based on the first node, which is the terminal node of the first tree. In the same manner as the above-described method, the processor 110 may generate the first vector based on the third nodes 710, 720, 730, 740, and 750, which are ancestor nodes of the first node. The third node 710 may be a parent node of a terminal node corresponding to “beef” ingredients and a terminal node corresponding to “pork” ingredients. The third node 720 may be a parent node of the terminal node corresponding to the “chicken” ingredient and the terminal node corresponding to the “duck” ingredient. The third node 730 may be a parent node of the terminal node corresponding to the “pepper” ingredient and the terminal node corresponding to the “bean” ingredient. The third node 740 may be a parent node of the terminal node corresponding to the “fish” ingredient and the terminal node corresponding to the “crustacean” ingredient. The third node 750 may be the parent node of the terminal node corresponding to the “seaweed” food ingredient.

Although not shown in FIG. 7, the third node may be the parent node of the node 710 corresponding to the “mammal” ingredient and the node 720 corresponding to the “poultry” ingredient. Additionally, the third node may be a parent node of a node corresponding to an “animal” ingredient and a node corresponding to a “plant” ingredient. However, the third node cannot be the root node, and therefore cannot be the parent node of the node corresponding to the “land” ingredient and the node corresponding to the “sea” ingredient. However, examples of the third node are not limited to those described above.

FIG. 8 is a diagram for explaining an example of a fourth node according to an embodiment.

The tree 800 of FIG. 8 corresponds to the second tree 600 of FIG. 6, and redundant description will be omitted.

According to the above description, the processor 110 may generate the first vector based on the second node, which is the terminal node of the second tree. In the same manner as the above-described method, the processor 110 may generate the first vector based on the fourth nodes 810, 820, and 830, which are ancestor nodes of the second node. The fourth node 810 may be a parent node of a terminal node corresponding to a “boiling” recipe, a terminal node corresponding to a “steaming” recipe, and a terminal node corresponding to a “baking” recipe. The fourth node 820 may be the parent node of the terminal node corresponding to the “fermentation” recipe and the terminal node corresponding to the “radish” recipe. When the parent node of the terminal node 830 is the root node, the first vector may be generated based on the second node that is the terminal node 830.

Referring again to FIG. 3, in step 320, the processor 110 may derive a plurality of second vectors whose similarity to each of the plurality of first vectors is greater than or equal to a preset value.

As an example, the processor 110 stores the first vector {1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, corresponding to the “small” ingredients and the “boil” recipe. , 0, 0, 0} and a second vector whose cosine similarity is greater than or equal to a preset value can be derived. At this time, the food corresponding to the derived second vector may correspond to the food whose category is most similar to the food corresponding to the “beef” ingredient and the “boiling” recipe.

As another example, the processor 110 stores the first vector {0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, corresponding to the “fish” ingredient and the “fermentation” recipe. , 1, 0, 0} and a second vector whose cosine similarity is greater than or equal to a preset value can be derived. At this time, the food corresponding to the derived second vector may correspond to the food whose category is most similar to the food corresponding to the “fish” ingredient and the “fermentation” recipe.

In step 330, the processor 110 generates a survey list including foods corresponding to each of the plurality of second vectors.

The foods corresponding to each of the plurality of second vectors may be foods included in the food category corresponding to each of the plurality of first vectors.

As an example, when the plurality of first vectors correspond to a first node that is one terminal node included in the first tree and a second node that is one terminal node included in the second tree, the plurality of second vectors The foods corresponding to each may be foods included in a food category corresponding to an ingredient corresponding to one terminal node included in the first tree and a recipe corresponding to one terminal node included in the second tree. Here, since the foods corresponding to each of the plurality of second vectors cannot correspond to the same ingredients and recipes, they are all included in different food categories. According to FIGS. 5 and 6 described above, each of the foods corresponding to each of the plurality of second vectors may be included in 54 food categories.

As another example, when one or more of the plurality of first vectors correspond to a third node that is an ancestor node of the first node and a fourth node that is an ancestor node of the second node, each of the plurality of second vectors corresponds to The foods may be foods included in a food category corresponding to ingredients corresponding to the third node and recipes corresponding to the fourth node. According to FIGS. 7 and 8 described above, each of the foods corresponding to each of the plurality of second vectors may be included in 15 food categories.

Meanwhile, the survey list may investigate the user's preferences for foods corresponding to each of a plurality of second vectors. For example, the survey list may survey the user's preferences for beef radish soup, grilled deodeok, seasoned green onions, etc., corresponding to each of the plurality of second vectors. The survey list generated according to one embodiment can more accurately determine the user's preference for all food categories, prevents the same user from repeatedly surveying foods belonging to similar food categories, and eliminates some duplicates. It is possible to prevent the interpretation of the survey from being ambiguous as users respond with conflicting preferences for foods belonging to the same food category. However, the example of the survey list is not limited to the above.

Hereinafter, with reference to FIG. 9, another example of a method for generating a survey list for building a user food profile will be described.

Figure 9 is a flowchart illustrating another example of a method for generating a survey list for building a user food profile according to an embodiment.

Meanwhile, steps 910 to 930 of FIG. 9 each correspond to steps 310 to 330 of FIG. 3 . Accordingly, overlapping contents of steps 910 to 930 of FIG. 9 are omitted.

In step 940, the processor 110 may generate an additional survey list based on response information corresponding to the survey list.

Here, the response information corresponding to the survey list may include responses made by the user who has received the survey list generated by the processor regarding the foods included in the survey list. As an example, response information may include the user's preferences for foods included in the survey list. As another example, the response information may include the user's preferences, such as “I like” beef radish soup, “I don't like” “grilled deodeok”, and “it's the best” for “seasoned green onions.” However, examples of response information are not limited to the above.

Hereinafter, with reference to FIG. 10, an example in which a processor generates an additional survey list will be described.

FIG. 10 is a flowchart illustrating an example in which a processor generates an additional survey list according to an embodiment.

In step 1010, the processor 110 may generate a fifth tree regarding preferred ingredients and a sixth tree regarding preferred recipes based on response information corresponding to the survey list.

The processor 110 may generate a fifth tree regarding preferred ingredients and a sixth tree regarding preferred recipes by identifying the user's preferred ingredients and preferred recipes based on the response information. For example, if the response information includes the user's preference for beef radish soup, “It's the best,” the processor 110 creates a fifth tree with the “beef” ingredient as the root node and the “boiling” recipe as the root node. A sixth tree can be created.

Hereinafter, with reference to FIGS. 11 and 12 , an example of a fifth tree related to preferred ingredients and an example of a sixth tree related to preferred recipes generated by the processor will be described.

Figure 11 is a diagram for explaining an example of a fifth tree related to preferred food ingredients according to an embodiment.

The processor 110 generates a fifth tree 1110 with the “mammal” ingredient as the root node when the response information includes the user's preference of “it is the best” regarding the food corresponding to the “mammal” ingredient. can do. However, the example of the fifth tree is not limited to the above.

Figure 12 is a diagram for explaining a sixth tree related to preferred recipes according to an embodiment.

The processor 110 generates a sixth tree 1210 with the “heating” recipe as the root node when the response information includes a preference of “it is the best” regarding the food corresponding to the user’s “heating” recipe. can do. However, the example of the sixth tree is not limited to the above.

Referring again to FIG. 10 , in step 1020, the processor 110 may generate a plurality of third vectors corresponding to one or more food ingredients and one or more recipes based on the fifth tree and the sixth tree.

The method in which the processor 110 generates a plurality of third vectors corresponding to one or more food ingredients and one or more recipes based on the fifth tree and the sixth tree includes the processor 110 described above using the first tree and the third tree. It is the same as the method of generating a plurality of first vectors corresponding to one or more food ingredients and one or more recipes based on the tree.

In step 1030, the processor 110 may derive a plurality of fourth vectors whose similarity to each of the plurality of third vectors is greater than or equal to a preset value.

The method by which the processor 110 derives a plurality of fourth vectors whose similarity with each of the plurality of third vectors is greater than or equal to a preset value is that the processor 110 as described above derives the similarity with each of the plurality of first vectors. This is the same as the method of deriving a plurality of second vectors that are greater than or equal to a preset value.

In step 1040, the processor 110 may generate an additional survey list including foods corresponding to each of the plurality of fourth vectors.

The method for the processor 110 to generate an additional survey list including foods corresponding to each of a plurality of fourth vectors includes the above-described processor 110 generating a questionnaire including foods corresponding to each of the plurality of second vectors. It is the same as the method for creating a list.

Meanwhile, the additional survey list including foods corresponding to each of the plurality of fourth vectors may be a survey list generated by reflecting the user's preferences. According to the present disclosure described above, a more specific and detailed user's preferred food category can be accurately confirmed through an additional questionnaire list.

As described above, the processor 110 generates a survey list including foods corresponding to various food categories based on ingredient and recipe information for a plurality of foods. Additionally, the processor 110 may generate an additional survey list reflecting the user's preferences through the generated survey list, and build the user's food profile through the survey list and the additional survey list.

Meanwhile, the above-described method can be written as a program that can be executed on a computer, and can be implemented in a general-purpose digital computer that operates the program using a computer-readable recording medium. Additionally, the data structure used in the above-described method can be recorded on a computer-readable recording medium through various means. The computer-readable recording media includes storage media such as magnetic storage media (e.g., ROM, RAM, USB, floppy disk, hard disk, etc.) and optical read media (e.g., CD-ROM, DVD, etc.) do.

Meanwhile, the above-described method may be included and provided in a computer program product. Computer programs and products are commodities and can be traded between sellers and buyers. The computer program product may be distributed in the form of a machine-readable storage medium (e.g. compact disc read only memory (CD-ROM)) or through an application store (e.g. Play StoreTM) or between two user devices. It may be distributed in person or online (e.g., downloaded or uploaded). In the case of online distribution, at least a portion of the computer program product may be at least temporarily stored or temporarily created in a machine-readable storage medium, such as the manufacturer's server, the server of an application store, or the memory of a relay server.

Those skilled in the art related to the present embodiment will understand that the above-described substrate can be implemented in a modified form without departing from the essential characteristics. Therefore, the disclosed methods should be considered from an explanatory rather than a restrictive perspective, and the scope of rights is indicated in the claims, not the foregoing description, and should be interpreted to include all differences within the equivalent scope.

Claims (12)

generating a plurality of first vectors for virtual foods and a plurality of second vectors for real foods based on ingredient information and recipe information for a plurality of foods;
Deriving a plurality of second vectors whose similarity to each of the plurality of first vectors is equal to or greater than a preset value; and
Comprising: generating a survey list including foods corresponding to each of the plurality of second vectors whose similarity is greater than or equal to a preset value,
The first vectors are,
It is created based on one ingredient and one recipe,
The second vectors are,
A method of creating a survey list for building a user food profile, which is created based on ingredients and recipes for one actual food. According to claim 1,
The step of generating a plurality of first vectors related to the virtual food and a plurality of second vectors related to the actual food,
Generating a first tree about ingredients and a second tree about recipes based on ingredient information and recipe information about the plurality of foods; and
A method comprising: generating the plurality of first vectors based on the first tree and the second tree. According to clause 2,
The first tree is,
Includes nodes set as ingredients for each of the plurality of foods,
The second tree is,
A method comprising nodes set to recipes for each of the plurality of foods. According to clause 3,
Each of the plurality of first vectors is,
A method corresponding to one node included in the first tree and one node included in the second tree. According to clause 3,
Each of the plurality of first vectors is,
A method corresponding to a first node that is one terminal node included in the first tree and a second node that is one terminal node included in the second tree. According to clause 5,
At least one of the plurality of first vectors is,
Corresponds to a third node that is the ancestor node of the first node and a fourth node that is the ancestor node of the second node,
The third node and fourth node are,
A method that is not the root node. delete According to claim 1,
Each of the plurality of second vectors is,
A method, which is generated by using a third tree regarding ingredients generated based on ingredient information regarding one food and a fourth tree relating to recipes generated based on recipe information relating to the one food. According to claim 1,
The method further includes generating an additional survey list based on response information corresponding to the survey list. According to clause 9,
The step of creating the additional survey list is,
generating a fifth tree regarding preferred ingredients and a sixth tree relating to preferred recipes based on response information corresponding to the survey list;
generating a plurality of third vectors for virtual food and a plurality of fourth vectors for real food based on the fifth tree and the sixth tree;
Deriving a plurality of fourth vectors whose similarity to each of the plurality of third vectors is greater than or equal to a preset value; and
A step of generating an additional survey list including foods corresponding to each of the plurality of fourth vectors whose similarity is greater than or equal to a preset value,
The third vectors are,
It is created based on one of the preferred ingredients and a recipe of one of the preferred recipes,
The fourth vectors are,
A method created based on ingredients and recipes for an actual food. at least one memory; and
At least one processor;
The at least one processor,
Generating a plurality of first vectors for virtual foods and a plurality of second vectors for real foods based on ingredient information and recipe information for a plurality of foods,
Deriving a plurality of second vectors whose similarity to each of the plurality of first vectors is greater than or equal to a preset value,
Generating a survey list including foods corresponding to each of the plurality of second vectors whose similarity is greater than or equal to a preset value,
The first vectors are,
It is created based on one ingredient and one recipe,
The second vectors are,
A computing device created based on ingredients and recipes for an actual food. A computer-readable recording medium recording a program for executing the method according to claim 1 on a computer.