KR102486324B1 - Method for matching buyer and seller for vehicle - Google Patents

Abstract

In accordance with some embodiments of the present disclosure, a method for matching buyers and sellers performed by a server including at least one processor is disclosed. The method for matching a buyer and a seller may include receiving first consultation information for reserving a consultation from a buyer terminal of a target buyer; determining at least one seller among the plurality of sellers based on at least one matching algorithm using the first consultation information and seller information of each of the plurality of predetermined sellers; Transmitting target seller information related to the determined at least one seller to the buyer terminal; and transmitting reservation information for reserving the consultation to a buyer terminal and a seller terminal of the first seller based on a selection signal from the buyer terminal selecting a first seller from among the at least one seller. can do.

Description

A method for matching buyers and sellers of vehicles {METHOD FOR MATCHING BUYER AND SELLER FOR VEHICLE}

The present disclosure relates to a method for matching a buyer and a seller of a vehicle, and more specifically, to a method for matching a buyer and a seller in consideration of a buyer's buying propensity and a seller's selling propensity.

Recently, various services for purchasing a vehicle online have been created. Buyers who want to purchase a vehicle can reserve a visit to a store that sells the vehicle they want to purchase using the service. In addition, the buyer may or may not purchase the vehicle after visiting the store and confirming the actual vehicle.

In this process, buyers may find a seller who sells the vehicle at a lower price than other sellers. This may be because the price of a vehicle is determined by the brand of the vehicle, but sellers offer various discount conditions in order to earn their own profits by selling the vehicle to more buyers.

Alternatively, some buyers may want to receive information based on expert knowledge about the vehicle they intend to purchase. Still others may prioritize the location of the store over the price of the vehicle. For example, some shoppers may find stores convenient to visit where they live. Or some buyers may wish to purchase a used car. These buyers may consider meeting a reliable seller as a top priority, even if the store is located somewhat far away.

However, conventional services do not properly grasp these various tendencies of buyers, and simply connect a seller selling a vehicle to a buyer.

Republic of Korea Patent No. 10-1930447

The present disclosure intends to provide a method for matching a buyer and a seller in consideration of a buyer's purchasing propensity and a seller's selling propensity.

The technical problems of the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description.

According to an embodiment of the present disclosure for solving the above problems, a method for matching buyers and sellers performed by a server including at least one processor is disclosed. The method for matching a buyer and a seller may include receiving first consultation information for reserving a consultation from a buyer terminal of a target buyer; determining at least one seller among the plurality of sellers based on at least one matching algorithm using the first consultation information and seller information of each of the plurality of predetermined sellers; Transmitting target seller information related to the determined at least one seller to the buyer terminal; and transmitting reservation information for reserving the consultation to a buyer terminal and a seller terminal of the first seller based on a selection signal from the buyer terminal selecting a first seller from among the at least one seller. can include

In addition, the first consultation information may include brand information about a brand that the target buyer wants to purchase, vehicle type information about a vehicle model, location information about a current location of the target buyer, date information for reserving a consultation, and reservation for a consultation. includes at least one of evaluation item information for selecting at least one evaluation item to indicate the target buyer's propensity, and purpose information for the purpose of reserving time information for counseling, wherein the at least one evaluation item is discounted or not A first item related to, a second item related to the location of the store the target buyer will visit, a third item related to the career of the seller, a fourth item related to the degree of congestion of the store the target buyer will visit, and the seller It may include at least one of the fifth items related to the expertise of.

In addition, the evaluation item information includes priority information indicating an order in which the target purchaser selects each of the at least one evaluation item, and at least one item using the first consultation information and seller information of each of a plurality of predetermined sellers. Determining at least one seller from among the plurality of sellers, based on a matching algorithm of, may include determining the target among a plurality of other buyers based on a first matching algorithm using evaluation item information included in the first consultation information. determining at least one other buyer having a propensity similar to the propensity of the buyer; and determining at least one seller from among the plurality of sellers based on a second matching algorithm using other evaluation item information of the at least one other buyer, the evaluation item information, and the seller information.

에 기초하여 상기 타깃 구매자 및 복수의 다른 구매자의 구매 성향 유사도를 결정하고, 상기

는 상기 타깃 구매자와

번째 다른 구매자와의 구매 성향 유사도이고, 상기

은 상기 적어도 하나의 평가 항목의 개수이고, 상기

는 자연수이고, 상기

는 상기 타깃 구매자가

번째 평가 항목에 대해 결정한 우선순위 엘리먼트이고, 상기

는

번째 다른 구매자가

번째 평가 항목에 대해 결정한 우선순위 엘리먼트이고, 상기 제 1 상담 정보에 포함된 평가 항목 정보를 이용하는 제 1 매칭 알고리즘에 기초하여, 복수의 다른 구매자 중 상기 타깃 구매자의 성향과 유사한 성향을 갖는 적어도 하나의 다른 구매자를 결정하는 단계는, 상기 구매 성향 유사도에 기초하여 복수의 다른 구매자 중 상기 적어도 하나의 다른 구매자를 결정할 수 있다.In addition, the first matching algorithm is Equation

determining similarity in purchasing propensity between the target buyer and a plurality of other buyers based on

is the target buyer and

th is the degree of similarity of purchasing propensity with other buyers,

Is the number of the at least one evaluation item,

is a natural number, and

is the target buyer

A priority element determined for the th evaluation item,

Is

the other buyer

It is a priority element determined for the th evaluation item, and based on a first matching algorithm using evaluation item information included in the first consultation information, at least one of a plurality of other buyers having a propensity similar to that of the target buyer In the determining of another buyer, the at least one other buyer among a plurality of other buyers may be determined based on the degree of similarity in purchasing propensity.

에 기초하여 복수의 판매자 각각에 대한 평가 점수를 결정하고, 상기

은

명의 판매자 중

번째 판매자에 대해 다른 구매자의 구매 성향 유사도가 반영된 평가 점수이고, 상기

및 상기

은 자연수이고, 상기

는 상기

번째 판매자에게 평점 후기를 작성한 다른 구매자의 총 인원 수이고, 상기

는 자연수이고, 상기

는 상기 타깃 구매자와

번째 다른 구매자의 구매 성향 유사도이고, 그리고 상기

는 상기

번째 판매자에 대한

번째 다른 구매자의 평점일 수 있다.In addition, the second matching algorithm is Equation

Determine the evaluation score for each of the plurality of sellers based on,

silver

of sellers

An evaluation score reflecting the similarity of purchasing propensity of other buyers for the second seller,

and above

is a natural number, and

said

The total number of other buyers who wrote rating reviews for the th seller,

is a natural number, and

is the target buyer and

th is the similarity of purchasing propensity of other buyers, and the

said

for the second seller

Second, it may be the rating of another buyer.

In addition, the step of determining at least one seller from among the plurality of sellers based on a second matching algorithm using the second consultation information of the at least one other buyer, the first consultation information, and the seller information, At least one seller from among the plurality of sellers may be determined based on the evaluation score for each seller.

The technical solutions obtainable in the present disclosure are not limited to the above-mentioned solutions, and other solutions not mentioned will become clear to those skilled in the art from the description below. You will be able to understand.

According to some embodiments of the present disclosure, it is possible to provide a method for matching a seller matching a buyer's purchasing propensity.

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

Various aspects are now described with reference to the drawings, wherein like reference numbers are used to collectively refer to like elements. In the following embodiments, for explanation purposes, numerous specific details are set forth in order to provide a thorough understanding of one or more aspects. However, it will be apparent that such aspect(s) may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing one or more aspects.
1 illustrates an exemplary system for performing a method for matching buyers and sellers in accordance with some embodiments of the present disclosure.
2 is a flowchart illustrating an example of a method in which a server matches a buyer and a seller according to some embodiments of the present disclosure.
3 is a flowchart illustrating an example of a method in which a server determines at least one seller using at least one matching algorithm according to some embodiments of the present disclosure.
4 is a diagram for explaining an example in which evaluation items are output to a purchaser terminal of a target purchaser according to some embodiments of the present disclosure.

Since the present invention can make various changes and have various embodiments, specific embodiments will be illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the present invention to specific embodiments, and should be understood to include all modifications, equivalents, and substitutes included in the spirit and scope of the present invention. Like reference numerals have been used for like elements throughout the description of each figure.

Terms such as first, second, A, B, etc. may be used to describe various components, but the components should not be limited by the terms. These terms are only used for the purpose of distinguishing one component from another. For example, a first element may be termed a second element, and similarly, a second element may be termed a first element, without departing from the scope of the present invention. The terms and/or include any combination of a plurality of related recited items or any of a plurality of related recited items.

It is understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, but other elements may exist in the middle. It should be. On the other hand, when an element is referred to as “directly connected” or “directly connected” to another element, it should be understood that no other element exists in the middle.

Terms used in this application are only used to describe specific embodiments, and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this application, the terms "include" or "have" are intended to designate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, but one or more other features It should be understood that the presence or addition of numbers, steps, operations, components, parts, or combinations thereof is not precluded.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related art, and unless explicitly defined in the present application, they should not be interpreted in an ideal or excessively formal meaning. don't

In the present disclosure, the server may match a buyer to purchase a vehicle and a seller to sell the vehicle. Here, the meaning of matching a buyer and a seller may be understood as meaning determining a seller based on information related to a buyer obtained from a buyer terminal. Specifically, the server may receive counseling information for reserving counseling from a purchaser terminal of a target purchaser. Here, the consultation information includes brand information on the brand that the buyer wants to purchase, vehicle type information on the vehicle type, location information on the current location of the buyer, date information for reserving consultation, time information for reserving consultation, and reservation for consultation. and evaluation item information for selecting at least one evaluation item to represent target purchaser's propensity. For example, when location information on the current location of the purchaser is received from the purchaser's terminal, the server may determine a seller selling a vehicle in a location close to the current location of the purchaser. The server may match the buyer and the seller by transmitting information related to the determined seller to the buyer terminal. Hereinafter, a method for matching a buyer and a seller by the server according to the present disclosure will be described with reference to FIGS. 1 to 4 .

1 illustrates an exemplary system for performing a method for matching buyers and sellers in accordance with some embodiments of the present disclosure.

Referring to FIG. 1 , the server 100 may include a control unit 110, a storage unit 120, and a communication unit 130. However, the above-described components are not essential to implement the server 100, so the server 100 may have more or fewer components than those listed above.

Server 100 may include any type of computer system or computer device, such as, for example, microprocessors, mainframe computers, digital processors, portable devices and device controllers, and the like.

The controller 110 may control overall operations of the server 100 . The controller 110 may provide or process appropriate information or functions by processing signals, data, information, etc. input or output through components of the server 100 or by running an application program stored in memory.

The control unit 110 may be composed of one or more cores, such as a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), a tensor processing unit (TPU), and the like. It may include a processor for data analysis of.

In the present disclosure, the controller 110 may receive first consultation information for reserving a consultation from the purchaser terminal 200 of the target purchaser through the communication unit 130 . The first consultation information includes, for example, brand information on a brand that the target buyer wants to purchase, vehicle type information about the type of car that the target buyer wants to purchase, location information about the current location of the target buyer, and date information for making a consultation reservation. , at least one of time information for reserving counseling or purpose information for the purpose of reserving counseling. When the first consultation information is received, the controller 110 may determine at least one seller among a plurality of sellers based on at least one matching algorithm using the first consultation information and seller information of each of a plurality of predetermined sellers. there is. Here, the seller information may include information related to the individual seller, information about the exhibition hall where the seller works, and information about reviews left by other buyers matched with the seller.

For example, seller information related to an individual seller may include the seller's name, age, gender, photo, career, title, or non-working days of the seller. Alternatively, the seller information may include information about whether the seller is a certified salesperson, a recently sold vehicle, or a vehicle mainly sold. Alternatively, the seller information may include affiliation information about the affiliation in which the seller works. Affiliation information may include information about a brand.

Information about the exhibition hall where the seller works may include information about the location of the exhibition hall, information about vehicles currently on display, information about business hours, information about congestion, information about vehicles on display, and the like. The exhibited vehicle information may include, for example, model name/trim name of the displayed vehicle, year model, engine, engine displacement, information on fuel efficiency, and vehicle photos.

Information on reviews left by other buyers may include information on satisfaction with the exhibition hall where the seller works, information on satisfaction with vehicles purchased from the seller, and information on satisfaction with the seller. Here, the satisfaction information about the exhibition hall may be information obtained by evaluating the cleanliness, comfort, or congestion of the exhibition hall. The satisfaction information with respect to the seller may be information obtained by evaluating the manner or kindness of the seller.

When at least one seller is determined, the communication unit 130 may transmit target seller information related to at least one seller to the buyer terminal. The target seller information may include information related to at least one individual seller, information about an exhibition hall where at least one seller works, information about reviews left by other buyers matched with the at least one seller, and the like. As at least one seller is matched using the first consultation information and the seller information of each of a plurality of predetermined sellers, satisfaction with the service of the target buyer may be satisfied. Hereinafter, an example of matching a target buyer and at least one seller by the controller 110 will be described in detail with reference to FIG. 2 .

The storage unit 120 may include a memory and/or a permanent storage medium. Memory is a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (eg SD or XD memory, etc.), RAM (Random Access Memory, RAM), SRAM (Static Random Access Memory), ROM (Read-Only Memory, ROM), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk, optical disk At least one type of storage medium may be included.

The storage unit 120 may include one or more memories including a buffer cache. Here, the memory is a main storage device directly accessed by the control unit 110, such as random access memory (RAM) such as dynamic random access memory (DRAM) and static random access memory (SRAM), and is powered by It may refer to a volatile storage device in which stored information is instantaneously erased when turned off, but is not limited thereto. This memory can be operated by the controller 110. The memory includes a buffer cache, and data may be stored in data blocks of the buffer cache. The data may be recorded in the storage unit 120 by a background process.

The storage unit 120 may store any type of information generated or determined by the controller 110 and any type of information received by the communication unit 130 . For example, the storage unit 120 may store seller information of each of a plurality of sellers.

The communication unit 130 may be used between the server 100 and the communication system, between the server 100 and the purchaser terminal 200, between the server 100 and the seller terminal 300, or between the server 100 and the network 400. It may contain one or more modules that enable communication. The communication unit 130 may include at least one of a wired Internet module and a wireless Internet module.

The purchaser terminal 200 may include typical computer hardware (eg, computer processor, memory, storage, input and output devices, and other components of conventional computing devices; electronic communication devices such as routers, switches, etc.; networks; A combination of electronic information storage systems such as network-attached storage (NAS) and storage area network (SAN) and computer software (ie, instructions that cause a computing device to function in a particular way). may be used to achieve desired system performance. For example, the purchaser terminal 200 includes a PC (personal computer), a notebook (note book), a mobile terminal, a smart phone (smart phone), a tablet PC (tablet pc), etc. owned by the user. and may include all types of terminals capable of accessing wired/wireless networks. Alternatively, the purchaser terminal 200 may include any type of computer system or computer device, such as a microprocessor, digital processor, device controller, and user terminal.

In the present disclosure, the purchaser terminal 200 may be a device associated with a target purchaser planning to purchase a vehicle. The target purchaser may transmit first consultation information for reserving consultation to the server 100 through the purchaser terminal 200 .

For example, the first consultation information includes brand information about the brand that the target buyer wants to purchase, vehicle type information about the vehicle type, location information about the current location of the target purchaser, location information about the location the target purchaser wants to visit, It may include at least one of date information for reserving consultation, time information for reserving consultation, purpose information for the purpose of reserving consultation, and evaluation item information for selecting at least one evaluation item to indicate the propensity of a target buyer. can

The purchaser terminal 200 may receive target seller information related to at least one seller from the server 100 . For example, the buyer terminal 200 may transmit at least one of information related to at least one individual seller, information about an exhibition hall where at least one seller works, and information about reviews left by other buyers matched with the at least one seller. It is possible to receive the target seller information including. When the target seller information is received, the purchaser terminal 200 may receive an input for selecting a first seller from among at least one seller from the target buyer. The buyer terminal 200 may reserve a consultation between the target buyer and the first seller by transmitting a selection signal for selecting the first seller to the server 100 . When counseling is reserved, the target buyer may visit the exhibition hall where the first seller works and receive counseling about vehicle purchase. Depending on the embodiment, the counseling may be non-face-to-face counseling. In this case, the target buyer and the first seller may conduct non-face-to-face consultation at a predetermined time.

Merchant terminal 300 may include typical computer hardware (eg, computer processor, memory, storage, input and output devices, and other components of existing computing devices; electronic communication devices such as routers, switches, etc.; networks; A combination of electronic information storage systems such as network-attached storage (NAS) and storage area network (SAN) and computer software (ie, instructions that cause a computing device to function in a particular way). may be used to achieve desired system performance. For example, the seller terminal 300 includes a PC (personal computer), a notebook (note book), a mobile terminal, a smart phone (smart phone), a tablet PC (tablet pc), etc. owned by the user. and may include all types of terminals capable of accessing wired/wireless networks. Alternatively, the vendor terminal 300 may include any type of computer system or computer device, such as a microprocessor, digital processor, device controller, and user terminal.

In the present disclosure, the seller terminal 300 may receive reservation information. Here, the reservation information may include information for the target buyer to reserve consultation with the first seller. For example, the reservation information may include a visit time of the target buyer, information on a vehicle the target buyer wants to consult with, or a vehicle currently owned by the target buyer.

The network 400 can be configured regardless of its communication form, such as wired and wireless, and includes a personal area network (PAN), a local area network (LAN), and a wide area network (WAN). It can be composed of various communication networks such as In addition, the network may be the known World Wide Web (WWW), or may use a wireless transmission technology used for short-range communication, such as Infrared Data Association (IrDA) or Bluetooth. The techniques described herein may be used in the networks mentioned above as well as other networks.

Hereinafter, a method for matching buyers and sellers performed by the server 100 will be described in detail.

2 is a flowchart illustrating an example of a method in which a server matches a buyer and a seller according to some embodiments of the present disclosure.

Referring to FIG. 2 , the communication unit 130 of the server 100 may receive first consultation information for reserving a consultation from the purchaser terminal 200 of the target purchaser (S110).

The first consultation information includes brand information about the brand that the target buyer wants to purchase, vehicle type information about the type of car that the target buyer wants to buy, location information about the current location of the target buyer, and location about the location the target buyer wants to visit. Information, date information for booking a consultation, time information for booking a consultation, purpose information for the purpose of booking a consultation, information about the target buyer's current vehicle, information indicating whether the target buyer will visit the showroom by vehicle may include at least one of them.

According to some embodiments of the present disclosure, the first consultation information may include evaluation item information for selecting at least one evaluation item to indicate the propensity of the target purchaser. Here, the at least one evaluation item is a first item related to discount, a second item related to the location of a store the target buyer will visit, a third item related to the career of the seller, and a congestion level of the store the target buyer will visit. It may include at least one of a fourth item related to and a fifth item related to professional knowledge of a seller.

Specifically, the target purchaser provides brand information about the brand, vehicle type information about the vehicle type, location information about the current location of the target purchaser, location information about the location the target purchaser wants to visit, and consultation through the purchaser terminal 200. At least one of date information for making a reservation, time information for making a reservation for consultation, purpose information for the purpose of making a consultation reservation, information on the target buyer's current vehicle, and information indicating whether the target buyer will visit the exhibition hall by vehicle. can be entered. After inputting the above information, the target purchaser may select at least one evaluation item among the first to fifth items. The purchaser terminal 200 may generate evaluation item information based on at least one evaluation item selected by the target purchaser. The purchaser terminal 200 may transmit first consultation information including generated evaluation item information to the server 100 .

For example, the evaluation item information may include information indicating that a target purchaser has selected a first item, a second item, and a fourth item among a plurality of evaluation items. For another example, the evaluation item information may include information indicating that a target purchaser has selected a first item, a second item, a third item, a fourth item, and a fifth item among a plurality of evaluation items.

According to some embodiments of the present disclosure, the evaluation item information may include priority information indicating an order in which each of the at least one evaluation item is selected by a target purchaser.

For example, the target purchaser may select a first item, a second item, and a fourth item from among a plurality of evaluation items. When selecting the first item, the second item, and the fourth item, the target purchaser may select the evaluation items in the order of the first item, the fourth item, and the second item. In this case, the priority information may indicate information that the first item is selected first, the fourth item is selected second, and the second item is selected third.

For another example, the target purchaser may select a first item, a second item, a third item, a fourth item, and a fifth item from among a plurality of evaluation items. When selecting the first to fifth items, the target purchaser may select evaluation items in the order of the fifth item, the fourth item, the third item, the second item, and the first item. In this case, the priority information may indicate information that the fifth item is selected first, the fourth item is second, the third item is third, the second item is fourth, and the first item is fifth.

The controller 110 may determine at least one seller from among the plurality of sellers based on at least one matching algorithm using the first consultation information and the seller information of each of the plurality of predetermined sellers (S210).

The at least one matching algorithm may include at least one of a collaborative filtering algorithm, a content based filtering algorithm, and a deep learning-based algorithm.

Here, the collaborative filtering algorithm may be an algorithm that automatically predicts interests of users according to preference information obtained from many users. The controller 110 determines at least one seller from among the plurality of sellers based on information related to each of the plurality of sellers and first consultation information obtained from buyers other than the target buyer who have a history of consulting with each of the plurality of sellers. can decide Information related to each of a plurality of sellers obtained from buyers other than the target buyer may include, for example, information about reviews left by other buyers on each of the plurality of sellers. The controller 110 may determine at least one seller from among a plurality of sellers based on a matching algorithm based on a collaborative filtering algorithm.

The content-based filtering algorithm may be an algorithm that recommends items similar to items that the user has experienced in the past. Based on the first consultation information, the controller 110 may determine a seller having a history of consultation with the target buyer. The controller 110 may determine a seller having a similar sales propensity to the determined seller as at least one seller.

The deep learning-based algorithm may be an algorithm that determines at least one seller through a neural network model trained to determine at least one seller from the first consultation information and seller information. The neural network model may be a network model based on Convolutional Neural Networks (CNN), Artificial Neural Network (ANN), Deep Neural Network (DNN), or Recurrent Neural Network (RNN). The neural network model may be a model that outputs at least one seller among a plurality of sellers when the first consultation information and seller information are input. Hereinafter, the deep learning-based algorithm may be implemented using conventional neural network models, and detailed descriptions thereof are omitted.

According to some embodiments of the present disclosure, the at least one matching algorithm may include a first matching algorithm and a second matching algorithm. The first matching algorithm may be an algorithm that determines at least one other buyer having a propensity similar to that of the target buyer among a plurality of other buyers by using evaluation item information included in the first consultation information. The second matching algorithm may be an algorithm that determines at least one seller among a plurality of sellers by using the determined other evaluation item information of at least one other buyer, evaluation item information of the target buyer, and seller information. Hereinafter, an example in which the controller 110 determines at least one seller by using the first matching algorithm and the second matching algorithm will be described with reference to FIG. 3 .

When at least one seller is determined, the communication unit 130 may transmit target seller information related to the determined at least one seller to the purchaser terminal 200 (S130). Here, the target seller information may include information related to an individual seller, information about an exhibition hall where the seller works, information about reviews left by other buyers matched with the seller, and the like. The communication unit 130 may cause the target seller information to be displayed on the purchaser terminal 200 by transmitting the target seller information to the purchaser terminal 200 .

The communication unit 130 transmits reservation information for making a consultation reservation to the buyer terminal 200 and the seller terminal of the first seller based on a selection signal from the buyer terminal 200 selecting a first seller from among at least one seller ( 300) can be transmitted (S140). Here, the reservation information may include a visit time of the target purchaser, information on a vehicle the target purchaser wants to consult with, or a vehicle currently owned by the target purchaser. The target purchaser can check the reservation information transmitted to the purchaser terminal 200 to confirm whether the reservation has been made correctly. The first seller may check the reservation information transmitted to the seller terminal 300 and prepare counseling for the target buyer. Depending on the embodiment, the first consultation information may include first date information for a target purchaser to reserve a consultation and first time information for a consultation reservation. The seller information may include information on a second date at which consultation with the first seller is possible and information on a second time at which consultation is possible. When determining at least one seller among a plurality of sellers, the controller 110 may determine at least one seller by considering first date information, first time information, second date information, and second time information. In other words, at least one seller determined by the controller 110 may be a seller capable of consulting on the date and time at which the target buyer wishes to reserve counseling. Accordingly, when a selection signal from the purchaser terminal 200 selecting the first seller is received, the controller 110 may transmit reservation information indicating that counseling is reserved to the seller terminal 300 of the first seller. Accordingly, a process of receiving an additional traffic light for confirming a reservation from the first seller may be omitted.

According to the configuration described above, the server 100 may receive the first consultation information from the purchaser terminal 200 . The server 100 may determine at least one seller among a plurality of sellers based on at least one matching algorithm using the first consultation information and seller information. As at least one seller is determined based on at least one matching algorithm using the first consultation information and seller information, at least one seller matching the propensity of the target buyer may be determined. Accordingly, the target buyer's satisfaction with the service can be satisfied.

Meanwhile, according to some embodiments of the present disclosure, at least one matching algorithm may include a first matching algorithm and a second matching algorithm. The server 100 may determine at least one seller using the first matching algorithm and the second matching algorithm. Hereinafter, an example of a method for the server 100 to determine at least one seller using at least one matching algorithm will be described with reference to FIG. 3 .

3 is a flowchart illustrating an example of a method in which a server determines at least one seller using at least one matching algorithm according to some embodiments of the present disclosure.

Referring to FIG. 3 , the controller 110 of the server 100 determines at least one of a plurality of buyers having a propensity similar to that of a target purchaser, based on a first matching algorithm using evaluation item information included in the first consultation information. One other buyer may be determined (S121).

Specifically, the evaluation item information includes the first item related to whether or not there is a discount, the second item related to the location of the store the target buyer will visit, the third item related to the seller's career, and the congestion level of the store the target buyer will visit. It may include information about selecting at least one evaluation item from among the related fourth item and the fifth item related to the professional knowledge of the seller. The controller 110 may determine at least one purchaser who made a selection similar to the evaluation item selected by the target purchaser.

Furthermore, the evaluation item information may include priority information indicating an order in which the target purchaser selects each of the at least one evaluation item. The controller 110 may determine at least one purchaser who made a selection similar to the evaluation items selected by the target purchaser and the order in which the at least one evaluation item was selected.

For example, the target purchaser may select a first item, a second item, and a fourth item among a plurality of evaluation items. When selecting the first item, the second item, and the fourth item, the target purchaser may select the evaluation items in the order of the first item, the fourth item, and the second item. In this case, the priority information may indicate information that the first item is selected first, the fourth item is selected second, and the second item is selected third. The controller 110 sets at least one other buyer who has selected the first item first, selected the fourth item second, and selected the second item third among the plurality of other buyers to have a propensity similar to that of the target buyer. It can be determined that there is at least one other buyer with

According to some embodiments of the present disclosure, the first matching algorithm may determine a similarity of purchasing propensity between a target buyer and a plurality of other buyers. Here, the purchasing propensity similarity may be a numerical value obtained by quantifying the degree of similarity between evaluation items selected by a target buyer and a plurality of other buyers. The controller 110 may determine at least one other buyer based on the similarity in purchasing propensity determined using the first matching algorithm.

Specifically, the first matching algorithm may determine the purchasing propensity similarity based on the following equation.

는 타깃 구매자와

번째 다른 구매자와의 구매 성향 유사도일 수 있다.

은 적어도 하나의 평가 항목의 개수일 수 있다. 예를 들어, 타깃 구매자가 선택한 평가 항목의 개수가 3개인 경우,

은 3일 수 있다. 다른 예를 들어, 타깃 구매자가 선택한 평가 항목의 개수가 5개인 경우,

은 5일 수 있다.

는 자연수일 수 있다.

는 타깃 구매자가

번째 평가 항목에 대해 결정한 우선순위 엘리먼트일 수 있다. 예를 들어, 타깃 구매자가 제 1 항목을 첫번째로 선택한 경우, 제 1 항목에 대한

는 1 일 수 있다. 다른 예를 들어, 타깃 구매자가 제 4 항목을 두번째로 선택한 경우, 제 4 항목에 대한

는 2일 수 있다.

는

번째 다른 구매자가

번째 평가 항목에 대해 결정한 우선순위 엘리먼트일 수 있다. 예를 들어, 다른 구매자가 제 1 항목을 첫번째로 선택한 경우, 제 1 항목에 대한

는 1 일 수 있다. 다른 예를 들어, 다른 구매자가 제 4 항목을 두번째로 선택한 경우, 제 4 항목에 대한

는 2일 수 있다. 상기의 수학식과 같이

는

의 합을

와 곱한 값에

을 나누어서 결정될 수 있다.here,

with the target buyer

It may be similarity of purchase propensity with other buyers.

may be the number of at least one evaluation item. For example, if the number of evaluation items selected by the target buyer is 3,

may be 3. As another example, if the number of evaluation items selected by the target buyer is 5,

may be 5.

may be a natural number.

is the target buyer

It may be a priority element determined for the th evaluation item. For example, if the target buyer selects item 1 first,

can be 1. As another example, if the target buyer selects the fourth item as the second,

may be 2.

Is

the other buyer

It may be a priority element determined for the th evaluation item. For example, if another buyer selects item 1 as the first,

can be 1. In another example, if another buyer selects the fourth item as the second,

may be 2. as in the above formula

Is

the sum of

to the value multiplied by

can be determined by dividing

When the similarity in purchasing propensity is determined using the first matching algorithm, the controller 110 may determine at least one other buyer based on the determined similarity in purchasing propensity. For example, the controller 110 may determine a preset number of other buyers having a high purchasing propensity similarity among a plurality of other buyers. For another example, the controller 110 may determine at least one other purchaser having a purchasing propensity similarity of a predetermined value or more among a plurality of other purchasers.

When at least one other buyer is determined, the controller 110 selects at least one of the plurality of sellers based on a second matching algorithm using other evaluation item information of the at least one other buyer, evaluation item information of the target buyer, and seller information. A seller may be determined (S122).

For example, the controller 110 may determine evaluation scores for each of a plurality of sellers by using the second matching algorithm. Here, the evaluation score may be a score determined to match a target buyer and seller. It can be understood that the higher the evaluation score of each of the plurality of sellers, the more suitable the seller is to the purchasing propensity of the target buyer. The controller 110 may determine at least one seller from among the plurality of sellers based on the determined evaluation scores for each of the plurality of sellers.

Specifically, the second matching algorithm may determine an evaluation score for each of a plurality of sellers based on the following equation.

은

명의 판매자 중

번째 판매자에 대해 다른 구매자의 구매 성향 유사도

가 반영된 평가 점수이고,

및

은 자연수일 수 있다.

는

번째 판매자에게 평점 후기를 작성한 다른 구매자의 총 인원 수 일 수 있다. 여기서, 평점 후기는

번째 판매자와 상담을 진행했거나 또는

번째 판매자로부터 차량을 구매한 다른 구매자가 상담을 진행한 이후 또는 차량을 구매한 이후 판매자를 평가한 점수일 수 있다.

는 자연수이고,

는 제 1 매칭 알고리즘을 이용하여 결정된 타깃 구매자와

번째 구매자의 구매 성향 유사도일 수 있다. 그리고

는

번째 판매자에 대한

번째 다른 구매자의 평점일 수 있다. 제어부(110)가 제 2 매칭 알고리즘을 이용하여 복수의 판매자 각각에 대한 평가 점수를 결정하는 경우, 평가 점수에는 타깃 구매자의 성향과 유사한 다른 판매자의 복수의 판매자 각각에 대한 평가 점수가 반영될 수 있다. 따라서, 결정된 평가 점수에 대한 신뢰도가 높을 수 있다. 제어부(110)는 결정된 복수의 판매자 각각에 대한 평가 점수에 기초하여, 복수의 판매자 중 적어도 하나의 판매자를 결정할 수 있다. 예를 들어, 제어부(110)는 복수의 판매자 중 평가 점수가 높은 사전 설정된 인원의 판매자를 결정할 수 있다. 다른 예를 들어, 제어부(110)는 복수의 판매자 중 기 설정된 값 이상의 평가 점수를 갖는 적어도 하나의 판매자를 결정할 수 있다. 상기의 수학식과 같이

은

에

를 가중치로 반영한 값을

로 나누어서 결정될 수 있다.here,

silver

of sellers

Similarity in purchasing propensity of other buyers to the second seller

is the evaluation score reflected,

and

may be a natural number.

Is

It may be the total number of other buyers who wrote rating reviews for the th seller. Here, the ratings are

You have consulted with the second seller, or

It may be a score evaluated by another buyer who has purchased a vehicle from the th seller after consulting or purchasing a vehicle.

is a natural number,

Is the target buyer determined using the first matching algorithm and

It may be the similarity of the purchase propensity of the second buyer. And

Is

for the second seller

Second, it may be the rating of another buyer. When the controller 110 determines an evaluation score for each of a plurality of sellers by using the second matching algorithm, an evaluation score for each of a plurality of sellers of other sellers similar to the propensity of the target buyer may be reflected in the evaluation score. . Accordingly, reliability of the determined evaluation score may be high. The controller 110 may determine at least one seller from among the plurality of sellers based on the determined evaluation scores for each of the plurality of sellers. For example, the controller 110 may determine a preset number of sellers having high evaluation scores among a plurality of sellers. For another example, the controller 110 may determine at least one seller having an evaluation score equal to or higher than a preset value among a plurality of sellers. as in the above formula

silver

to

the value reflected as a weight

It can be determined by dividing by

According to the configuration described above, the server 100 may determine at least one seller using the first matching algorithm and the second matching algorithm. The first matching algorithm may be an algorithm that determines at least one other buyer having a propensity similar to that of the target buyer among a plurality of other buyers by using evaluation item information included in the first consultation information. The second matching algorithm may be an algorithm that determines at least one seller among a plurality of sellers by using the determined other evaluation item information of at least one other buyer, evaluation item information of the target buyer, and seller information. In short, in determining at least one seller, the server 100 may use evaluation item information, first consultation information, and seller information of other buyers determined to have similar inclinations to the target buyer. Accordingly, at least one seller that the target buyer receives from the server 100 may be a seller that matches the propensity of the target buyer.

Meanwhile, according to some embodiments of the present disclosure, the first consultation information may include evaluation items selected from at least one evaluation item. And, the evaluation items are the first item related to discount, the second item related to the location of the store the target buyer will visit, the third item related to the career of the seller, and the congestion level of the store the target buyer will visit. At least one of the fourth item and the fifth item related to the expertise of the seller may be included. Hereinafter, an example in which evaluation items according to the present disclosure are displayed on the purchaser terminal 200 of a target purchaser will be described with reference to FIG. 4 .

4 is a diagram for explaining an example in which evaluation items according to some embodiments of the present disclosure are displayed on a purchaser terminal of a target purchaser.

4(a) to 4(c) may be screens displayed on the display unit of the purchaser terminal 200.

Referring to (a) of FIG. 4 , the display unit of the purchaser terminal 200 may include a first area 310 . The first area 310 may be an area where at least one evaluation item is displayed. For example, in the first region 310, at least one evaluation item includes a first item related to whether or not there is a discount, a second item related to the location of a store that the target buyer will visit, and a third item related to the seller's career. , a fourth item related to the degree of congestion of a store to be visited by the target buyer and a fifth item related to the professional knowledge of the seller may be displayed. The first area 310 may include an indicator displayed on one side of the first to fifth items. Here, the indicator may be for providing information to the user. In addition, a predetermined operation may be performed on the area where the indicator is displayed based on a user's touch input. For example, the target purchaser may select the first indicator 311 related to the first item. The purchaser terminal 200 may display an indication indicating that the first item is selected on the first indicator 311 based on an input from the target purchaser selecting the first indicator 311 . When an input for selecting the first indicator 311 is received, the purchaser terminal 200 may generate priority information indicating that the first item is selected first.

Referring to (b) of FIG. 4 , the target purchaser may select a fourth indicator 312 related to the fourth item. The purchaser terminal 200 may display an indication indicating that the fourth item is selected on the fourth indicator 312 based on an input from the target purchaser selecting the fourth indicator 312 . When an input for selecting the fourth indicator 312 is received, the purchaser terminal 200 may update priority information indicating that the fourth item is selected second.

Referring to (c) of FIG. 4 , the target purchaser may select the second indicator 313 related to the second item. The purchaser terminal 200 may display an indication indicating that the second item is selected on the second indicator 313 based on an input from the target purchaser selecting the second indicator 313 . When an input for selecting the second indicator 313 is received, the purchaser terminal 200 may update priority information indicating that the second item is selected third. The purchaser terminal 200 may transmit first consultation information including the generated priority information to the server 100 . Accordingly, the server 100 may determine at least one other buyer having a similar propensity to the target buyer through a first matching algorithm using the priority information.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art of this disclosure, and the general principles defined herein may be applied to other embodiments without departing from the scope of this disclosure. Thus, the present disclosure is not to be limited to the embodiments presented herein, but is to be interpreted in the widest scope consistent with the principles and novel features presented herein.

Claims (5)

A method for matching buyers and sellers performed by a server comprising at least one processor, comprising:
Receiving first consultation information for reserving consultation from a purchaser terminal of a target purchaser;
determining at least one seller among the plurality of sellers based on at least one matching algorithm using the first consultation information and seller information of each of the plurality of predetermined sellers;
Transmitting target seller information related to the determined at least one seller to the buyer terminal; and
Transmitting reservation information for reserving the consultation to the buyer terminal and the seller terminal of the first seller based on a selection signal from the buyer terminal selecting a first seller from among the at least one seller;
including,
The first consultation information,
Brand information on the brand that the target buyer wants to purchase, vehicle type information on the vehicle type, location information on the current location of the target buyer, date information for reserving consultation, time information for reserving consultation Purpose of reserving consultation Includes purpose information and evaluation item information for selecting a plurality of evaluation items to indicate the propensity of the target purchaser,
The plurality of evaluation items,
The first item related to whether or not there is a discount, the second item related to the location of the store the target buyer will visit, the third item related to the career of the seller, and the fourth item related to the degree of congestion of the store the target buyer will visit and a fifth item relating to the seller's expertise;
The evaluation item information,
Includes priority information indicating an order in which the target purchaser selected each of the plurality of evaluation items;
Determining at least one seller among the plurality of sellers based on at least one matching algorithm using the first consultation information and seller information of each of the plurality of predetermined sellers,
determining at least one other buyer having a propensity similar to that of the target buyer among a plurality of other buyers based on a first matching algorithm using evaluation item information included in the first consultation information; and
determining at least one seller from among the plurality of sellers by using other evaluation item information of the at least one other buyer, the evaluation item information, and the seller information;
including,
The first matching algorithm is Equation

Determining the similarity of purchasing propensity of the target buyer and a plurality of other buyers based on;
remind

is the target buyer and

th is the degree of similarity of purchasing propensity with other buyers,

Is the number of the plurality of evaluation items,

is a natural number, and

is the target buyer

A priority element determined for the th evaluation item,

Is

the other buyer

It is a priority element determined for the th evaluation item,
Determining at least one other buyer having a propensity similar to that of the target buyer among a plurality of other buyers based on a first matching algorithm using evaluation item information included in the first consultation information,
determining the at least one other purchaser from among a plurality of other purchasers based on the purchasing propensity similarity.
delete delete delete According to claim 1,
The second matching algorithm is Equation

Determine the evaluation score for each of the plurality of sellers based on,
remind

silver

of sellers

An evaluation score reflecting the similarity of purchasing propensity of other buyers for the second seller,

and above

is a natural number, and

said

The total number of other buyers who wrote rating reviews for the th seller,

is a natural number, and

is the target buyer and

th is the similarity of purchasing propensity of other buyers, and the

said

for the second seller

the ratings of other buyers,
Way.

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