Bilateral Marketing Method and Device for Supporting the Same

Abstract

Various embodiments relate to a viral marketing method and an apparatus for supporting the same. The electronic device includes a memory and a processor connected to the memory. The processor generates tag information related to a recommendation of a product by a specific first user as a response to a request of the specific first user among a plurality of users who are members of a service platform operated based on the electronic device, and transmits the tag information to the specific first user. Here, the tag information is distributable from the specific first user to at least one of (i) one or more of a first plurality of users excluding the specific first user among the plurality of users, or (ii) one or more of a second plurality of users who are not members of the service platform, and includes product information including an explanation about the product, price information related to the product, distribution information related to the distribution of a commission associated with the sale of the product, and specific first user information related to the specific first user.

Description

Technical Field

[0001] Various embodiments relate to a viral marketing method and an apparatus for supporting the same.

Background Art

[0002] In recent years, viral marketing, an advertising method on the Internet, has attracted attention as various marketing methods for promoting products. Viral marketing is different from conventional advertising in that a company does not directly conduct promotion, but spreads by word of mouth through online personal media such as consumers' e-mails and blogs. Therefore, an effective viral marketing method is required.

Summary of the Invention

Problems to be Solved by the Invention

[0003] Various embodiments can provide a viral marketing method and an apparatus for supporting the same.

[0004] The technical problems to be solved by various embodiments are not limited to those mentioned above, and other technical problems not mentioned can be considered by those having ordinary knowledge in the technical field from various embodiments described below.

Means for Solving the Problems

[0005] According to various embodiments, a method executed by an electronic device may be provided.

[0006] According to various embodiments, the method may include generating tag information related to a recommendation of a product by a specific first user as a response to a request of the specific first user among a plurality of users joined to a service platform operated based on the electronic device, and transmitting the tag information to the specific first user.

[0007] According to various embodiments, the tag information may be generated to satisfy at least one of the following: (i) being distributable from the specific first user to one or more of a first plurality of users excluding the specific first user among the plurality of users, or (ii) being distributable to one or more of a second plurality of users who are not members of the service platform.

[0008] According to various embodiments, the tag information may include product information including an explanation about the product, price information related to the product, distribution information related to the distribution of a commission associated with the sale of the product, and specific first user information related to the specific first user.

[0009] According to various embodiments, the price information may include information about the price of the product and information about the commission.

[0010] According to various embodiments, the commission may be set by the seller of the product.

[0011] According to various embodiments, the selling price at which the product is sold on the service platform may be set as the sum of the price of the product and the commission.

[0012] According to various embodiments, the distribution information may include: (i) first distribution ratio information regarding the ratio at which the commission is distributed to the operator operating the service platform; (ii) second distribution ratio information regarding the ratio at which the commission is distributed to the purchaser when the purchaser of the product is included in the first plurality of users or the second plurality of users; and (iii) third distribution ratio information regarding the ratio at which the commission is distributed to the specific first user when the purchaser is included in the first plurality of users or the second plurality of users.

[0013] According to various embodiments, when the purchaser is included in the first plurality of users or the second plurality of users, based on the allocation information included in the tag information, accumulating an amount calculated based on the commission and the second allocation ratio information to the specific first user, and accumulating an amount calculated based on the commission and the third allocation ratio information to the purchaser, may be further included.

[0014] According to various embodiments, the method may further include obtaining a recommended product list based on an output of a recommendation model obtained as a response to a request of a specific second user among the plurality of users and as a response to an input of information related to the specific second user, and transmitting recommended product information including the recommended product list to the specific second user.

[0015] According to various embodiments, the recommendation model may be preset based on machine learning applied to an artificial intelligence (AI) engine for obtaining the recommendation model.

[0016] According to various embodiments, the machine learning may be performed based on: (a) training the artificial intelligence engine based on training data for obtaining the recommendation model; (b) obtaining first feedback information regarding processed data output as a response to an input of the artificial intelligence engine with respect to test data for verifying the trained artificial intelligence engine; (c) updating the artificial intelligence engine based on the first feedback information; and (d) repeating (a) to (c), wherein each time (a) to (c) are repeated, a count value having an initial value of 0 is incremented by 1, and the process ends based on the count value matching a preset count threshold value.

[0017] According to various embodiments, the training data and the test data may be obtained based on learning data for obtaining the recommendation model.

[0018] According to various embodiments, the learning data may include information regarding the product recommendation history of each of the plurality of users, information regarding the purchase history of each of the plurality of users, information regarding the relevance between the information regarding the product recommendation history and the information regarding the purchase history, and profile information including the ages, genders, email addresses, mobile phones, and addresses of the plurality of users.

[0019] According to various embodiments, the information regarding the relevance may include an evaluation index as to whether the purchase of a specific product by a specific third user is derived from a specific product recommendation.

[0020] According to various embodiments, the evaluation index may be set based on the number of recipients who purchased the specific product among a plurality of recipients including the specific third user who received the specific product recommendation, a weight value related to the time interval from the time when the recipient who purchased the specific product received the specific product recommendation to the time when the recipient who purchased the specific product made the purchase of the specific product, the type of the specific product, the profile information of the user who sent the specific product recommendation, the number of recipients who repurchased the specific product within a preset time from the time when the recipient who purchased the specific product made the purchase of the specific product, the profile information of the repurchasing recipients, and the price of the specific product.

[0021] According to various embodiments, the weight value may be made smaller as the length of the time interval becomes longer.

[0022] According to various embodiments, the weight value may be made larger as the length of the time interval becomes shorter.

[0023] According to various embodiments, the method may further include, in response to the recommended product information, receiving from the specific second user, among a plurality of products sold on the service platform, purchases of one or more products not included in the recommended product list, and when purchases of all products included in the recommended product list are not made, generating second feedback information including information on the one or more products purchased by the specific second user, and updating the recommendation model based on the second feedback information.

[0024] According to various embodiments, the tag information may correspond to a Quick Response (QR) code.

[0025] According to various embodiments, the QR code may be set such that a recipient who receives the QR code can connect to a web page for purchasing the recommended product on the service platform via the QR code.

[0026] According to various embodiments, when the recipient is included in the second plurality of users and a request for a new subscription to the service platform is received from the recipient, a web page for subscribing to the service platform is displayed, and an ID (identifier) of the specific first user identified based on the specific first user information is autonomously input into a Recommender field included in the web page for subscribing.

[0027] According to various embodiments, an electronic device may be provided.

[0028] According to various embodiments, the electronic device may include a memory and one or more processors connected to the memory.

[0029] According to various embodiments, the one or more processors are configured to generate tag information related to a recommendation of a product by the specific first user and transmit the tag information to the specific first user as a response to a request of the specific first user among a plurality of users subscribed to a service platform operated based on the electronic device.

[0030] According to various embodiments, the tag information may be generated to satisfy at least one of (i) being distributable to one or more of a first plurality of users excluding the specific first user among the plurality of users from the specific first user, or (ii) being distributable to one or more of a second plurality of users not subscribed to the service platform.

[0031] According to various embodiments, the tag information may include product information including an explanation about the product, price information related to the product, distribution information related to distribution of a commission associated with sale of the product, and specific first user information related to the specific first user.

[0032] According to various embodiments, a non-transitory processor-readable medium may be provided that stores one or more instructions for causing one or more processors to perform operations.

[0033] According to various embodiments, the operations may include generating tag information related to a recommendation of a product by the specific first user and transmitting the tag information to the specific first user as a response to a request of the specific first user among a plurality of users subscribed to a service platform operated based on the electronic device including the one or more processors.

[0034] According to various embodiments, the tag information may be generated to satisfy at least one of the following: (i) being distributable from the specific first user to one or more of a first plurality of users excluding the specific first user among the plurality of users, or (ii) being distributable to one or more of a second plurality of users who are not members of the service platform.

[0035] According to various embodiments, the tag information may include product information including an explanation about the product, price information related to the product, distribution information related to the distribution of commissions associated with the sale of the product, and specific first user information related to the specific first user.

[0036] The various embodiments described above are only a part of the various embodiments, and various embodiments reflecting the technical features of the various embodiments of the present disclosure will be derived and understood by those skilled in the art based on the following detailed description.

Advantages of the Invention

[0037] According to various embodiments, a viral marketing method and an apparatus for supporting the same can be provided.

[0038] According to various embodiments, rewards are given to service platform users who participate in viral marketing, which can enhance the willingness of service platform users to participate in viral marketing and effectively conduct viral marketing.

[0039] By continuously participating in viral marketing by service platform users according to various embodiments, a seller can build his own distribution network and expect an increase in the seller's revenue.

[0040] According to various embodiments, recommended products for service platform users based on artificial intelligence can be recommended with high accuracy.

[0041] The effects obtained from various embodiments are not limited to the effects mentioned above, and other effects not mentioned will be clearly derived and understood by those with ordinary knowledge in the technical field based on the following detailed description.

Brief Description of the Drawings

[0042] The accompanying drawings, included as part of the detailed description to assist in understanding various embodiments, provide the various embodiments and, together with the detailed description, explain the technical features of the various embodiments.

[0043]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Modes for Carrying Out the Invention

[0044] The following embodiments are combinations of the components and features of various embodiments in a predetermined form. Each component or feature may be regarded as optional unless otherwise specified. Each component or feature may be implemented in a form that cannot be combined with other components or features. Also, various embodiments can be configured by combining some components and / or features. The order of operations described in various embodiments may be changed. Some configurations and features of one embodiment may be included in other embodiments, or may be replaced with corresponding configurations or features of other embodiments.

[0045] In the description of the drawings, procedures or steps that may obscure the gist of the present invention are omitted from the description, and procedures or steps that can be understood at the level of those with ordinary knowledge in the technical field are also omitted from the description.

[0046] Throughout the specification, when a part describes a certain component as "comprising or including", this means that, unless otherwise specified, it does not exclude other components, but may further include other components. Also, terms such as "... part", "... machine", "module", etc. described in this specification mean a unit that processes at least one function or operation, and this can be embodied by hardware, software, or a combination of hardware and software. Furthermore, "a", "one", "the", and similar related words can be used in the context of describing various embodiments (especially in the context of the following specification) to mean both singular and plural, unless otherwise indicated in this specification or clearly contradicted by the context.

[0047] Hereinafter, preferred embodiments according to various embodiments will be described in detail with reference to the accompanying drawings. Together with the accompanying drawings, the detailed description disclosed below is for explaining exemplary embodiments of various embodiments and not for showing the only embodiment.

[0048] In addition, the specific terms used in various embodiments are provided to assist in understanding the various embodiments, and the use of such specific terms may be changed to other forms without departing from the technical idea of the various embodiments.

[0049] 1. Embodiment of a service system

[0050] FIG. 1 is a diagram for explaining a service system in which an operation method of an apparatus for providing a service according to various embodiments can be embodied.

[0051] Referring to FIG. 1, a service system according to various embodiments can be embodied in various types of apparatuses. In the description of various embodiments, a service system according to various embodiments can be embodied by an electronic apparatus. For example, the service system may be embodied in the first device 100 and / or the second device 200. In other words, the first device 100 and / or the second device 200 can perform operations according to various embodiments based on the service system embodied in each device. On the other hand, the service system according to various embodiments is not limited to that shown in FIG. 1 and may be embodied in more diverse devices and / or servers.

[0052] The first device 100 according to various embodiments may be a first device 100 such as a smart device owned by a customer who intends to use a service system and / or a service platform operated by the service system. In this case, operations according to various embodiments described below may be embodied in the form of an operable application on the smart device. However, the first device 100 according to various embodiments is not limited thereto.

[0053] The second device 200 according to various embodiments may be a device that performs wireless and / or wired communication with the first device 100 and includes a database having a large storage capacity. For example, the second device 200 can also interact with a plurality of first devices 100. Although not shown, a separate device for controlling / managing the second device may be provided.

[0054] The service system according to various embodiments may include various modules for operation. The modules included in the service system may be computer code or one or more instructions embodied so that a physical device (e.g., the first device 100 and / or the second device 200) in which the service system is embodied (or included in a physical device) can perform a specified operation. In other words, the physical device in which the service system is embodied stores a plurality of modules in the form of computer code in a memory, and when the plurality of modules stored in the memory are executed, the plurality of modules can enable the physical device to perform a specified operation corresponding to the plurality of modules.

[0055] FIG. 2 is a diagram showing the configuration of the first device and / or the second device according to various embodiments.

[0056] Referring to FIG. 2, the first device and / or the second device may include an input / output unit 210, a communication unit 220, a database 230, and a processor 240.

[0057] The input / output unit 210 can be various interfaces and connection ports that receive user input or output information to the user. The input / output unit 210 can be divided into an input module and an output module. The input module receives user input from the user. The user input may be performed in various forms including key input, touch input, voice input, etc. Examples of the input module that can receive such user input include traditional forms of keypads, keyboards, mice, touch sensors that sense the user's touch, microphones that receive voice signal input, cameras that recognize gestures etc. through video (image) recognition, proximity sensors composed of illuminance sensors, infrared sensors etc. that sense the user's access (approach), motion sensors that recognize the user's motion through acceleration sensors, gyro sensors etc., and an inclusive concept that includes all various forms of input means that sense or receive various other forms of user input. Here, the touch sensor can be implemented as a piezoelectric or electrostatic touch sensor that senses touch through a touch panel or touch film attached to the display panel, an optical touch sensor that senses touch by an optical method, etc. In addition, the input module may be implemented in the form of an input interface (such as a USB port, PS / 2 port, etc.) that connects an external input device that receives user input instead of a device that senses user input by itself. Also, the output module can output various information and provide it to the user. The output module is an inclusive concept that includes all various forms of output means such as a display that outputs images, a speaker that outputs sounds, a haptic (tactile) device that generates vibrations, and other various forms of output means. In addition, the output module may be implemented in the form of a port-type output interface that connects the above-mentioned individual output means.

[0058] As an example, the output module in the form of a display can display text, still images, and videos. The display refers to a broad concept of an image display device that includes all forms of devices capable of executing various functions such as a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, a flat panel display (FPD), a transparent display, a curved display, a flexible display, a 3D display, a holographic display, a projector, and other image output functions. Such a display may be in the form of a touch display integrally configured with a touch sensor on the input module.

[0059] The communication unit 220 can communicate with external devices. Therefore, the first device and / or the second device can transmit and receive information with external devices via the communication unit. For example, the first device and / or the second device can communicate with external devices using the communication unit and share information stored and generated in the service system.

[0060] Here, communication, that is, the transmission and reception of data, may be either wired or wireless. Therefore, the communication unit may be composed of a wired communication module that connects to the Internet or the like via a local area network (LAN), a mobile communication module that connects to a mobile communication network via a mobile communication base station to transmit and receive data, a short-range communication module that uses a communication method in the wireless local area network (WLAN) series such as Wi-Fi or a communication method in the wireless personal area network (WPAN) series such as Bluetooth or Zigbee, a satellite communication module that uses a global navigation satellite system (GNSS) such as the global positioning system (GPS), or a combination thereof. The wireless communication technology used for communication may include narrowband Internet of Things (NB-IoT) for low-power communication. At this time, for example, the NB-IoT technology is an example of low-power wide area network (LPWAN) technology and can be implemented according to standards such as LTE category (Cat.) NB1 and / or LTE category (Cat.) NB2, and is not limited to the above-mentioned names. Furthermore or alternatively, the wireless communication technology implemented by wireless devices according to various embodiments can perform communication based on LTE-M technology. At this time, as an example, the LTE-M technology is an example of LPWAN technology and may be called by various names such as enhanced machine type communication (eMTC).For example, the LTE-M technology can be implemented by at least any one of various standards such as 1) LTE CAT 0, 2) LTE Cat M1, 3) LTE Cat M2, 4) LTE non-Bandwidth Limited (non-BL), 5) LTE-MTC, 6) LTE Machine Type Communication, and / or 7) LTE M, and is not limited to the names described above. Further, or alternatively, the wireless communication technology implemented in wireless devices according to various embodiments may include at least any one of Bluetooth, Zigbee, and Low Power Wide Area Network (LPWAN) considering low-power communication, and is not limited to the names described above. As an example, ZigBee technology can generate personal area networks (PANs) related to small / low-power digital communication based on various standards such as IEEE 802.15.4 and may be called by various names.

[0061] The database 230 can store various kinds of information. The database can store data temporarily or semi-permanently. For example, the database can store an operating system (OS) for starting the first device and / or the second device, data for hosting a website, and data related to a program or application (e.g., a web application) for braille generation. Also, as described above, the database can store the module in the form of computer code.

[0062] Examples of the database 230 include a hard disk drive (HDD), a solid state drive (SSD), flash memory, a read-only memory (ROM), a random access memory (RAM), and the like. Such a database may be provided in a built-in type or a removable type.

[0063] The processor 240 controls the overall operations of the first device 100 and / or the second device 200. For this reason, the processor 240 can perform calculations and processing of various information and control the operations of the components of the first device and / or the second device. For example, the processor 240 can execute a program or an application for providing a service. The processor 240 can be embodied as a computer or a similar device by hardware, software, or a combination thereof. Hardware-wise, the processor 240 can be provided in the form of an electronic circuit that processes electrical signals to execute a control function, and software-wise, the processor 240 can be provided in the form of a program that drives the hardware processor 240. On the other hand, in the following description, unless otherwise specified, the operations of the first device and / or the second device can be interpreted as being executed under the control of the processor 240. That is, when a module embodied in the service system is executed, the module can be interpreted as controlling the processor 240 to cause the first device and / or the second device to execute the following operations.

[0064] In summary, various embodiments may be embodied by various means. For example, various embodiments may be embodied by hardware, firmware, software, or a combination thereof.

[0065] In the case of implementation by hardware, the methods according to various embodiments may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, etc.

[0066] In the case of implementation by firmware or software, the methods according to various embodiments may be implemented in the form of modules, procedures, functions, etc. that execute the functions or operations described below. For example, the software code may be stored in a memory and driven by a processor. The memory may be disposed inside or outside the processor, and data can be transmitted and received between the processor and the memory by various known means.

[0067] 2. Configuration / Operation of the Device According to Various Embodiments

[0068] Hereinafter, various embodiments based on the above technical idea will be described in more detail. Unless they are mutually contradictory, all or some of the various embodiments described below can be combined to form other various embodiments, which will be clearly understood by those with ordinary knowledge in the technical field.

[0069] For the various embodiments described below, the content of Section 1 described above may be applicable. For example, in the various embodiments described below, undefined operations, functions, terms, etc. may be executed and described based on the content of Section 1.

[0070] Unless otherwise specified, in the description of various embodiments, "A / B / C" may mean "A and / or B and / or C".

[0071] Unless otherwise specified, in the description of various embodiments, "more than / above A" may be replaced with "above / more than A".

[0072] Unless otherwise specified, in the description of various embodiments, "less than / below B" may be replaced with "below / less than B".

[0073] Hereinafter, a user device according to an embodiment of the first device 100 according to various embodiments will be described. However, other devices that perform similar functions can also be regarded as the first device. For example, the first device may include, but is not limited to, a terminal, a smart phone, a laptop computer, a tablet PC, an e-book terminal, a digital broadcast terminal, a personal digital assistant (PDA), a portable multimedia player (PMP), a personal computer (PC), etc.

[0074] Hereinafter, a server according to an embodiment of the second device (200) according to various embodiments will be described. However, other devices that perform similar functions can also be regarded as the second device.

[0075] Unless otherwise specified, in the description of the following various embodiments, the information used / acquired / output / displayed, etc. may be one or more of the information directly identified / acquired by the first device and / or the second device, the information stored in the database included in the first device and / or the second device, and the information received by the first device and / or the second device from the server and / or other external devices.

[0076] Unless otherwise specified, in the description of various embodiments, the goods may include goods and / or products and / or services that are the subject of sales by the service platform according to various embodiments.

[0077] Unless otherwise specified, in the description of various embodiments, "transmission" not only means that specific information is directly distributed between specific devices, but also includes cases where specific information is distributed via a messenger or the like, specific information is broadcast, specific information is posted by a specific user to an SNS (Social Networking Service), social media, other web pages, etc., and other users view this information.

[0078] Unless otherwise specified, in the description of various embodiments, "user" may be replaced with "user device".

[0079] 2.1. Services Based on Tag Information

[0080] FIG. 3 is a diagram showing an example of an operation method of a user device and a server according to various embodiments.

[0081] FIG. 4 is a diagram showing an example of an operation method of a user device according to various embodiments.

[0082] FIG. 5 is a diagram showing an example of an operation method of a server according to various embodiments.

[0083] Referring to FIGS. 3 to 5, in operations 301, 401, and 501 according to various embodiments, the user device may send a request for generating tag information, and the server may receive this.

[0084] In operations 303 and 503 according to various embodiments, the server may generate tag information.

[0085] In operations 305, 405, and 505 according to various embodiments, the server may transmit / provide tag information, and the user device may receive it.

[0086] The more specific content of each operation according to various embodiments may be supplemented / executed / understood by the descriptions of the various embodiments described later.

[0087] According to various embodiments, the request for tag information and / or the request for tag information generation may be made by a specific first user among a plurality of users who subscribe to the service platform operated based on the server.

[0088] According to various embodiments, the tag information may be associated with a specific first user's recommendation of a specific product. According to various embodiments, the tag information may be generated so as to be distributable to one or more of a first plurality of users excluding the specific first user among a plurality of users who subscribe to the service platform from the specific first user. And / or, according to various embodiments, the tag information may be generated so as to be distributable to one or more of a second plurality of users who do not subscribe / are not subscribed to the service platform from the specific first user.

[0089] According to various embodiments, the recipient who receives the tag information from the specific first user may connect to the web page of the service platform for the sale of the product recommended by the specific first user through the tag information. And / or, according to various embodiments, the recipient who receives the tag information from the specific first user may also forward it to a third party. And / or, according to various embodiments, the third party who receives the tag information may also forward it to another third party.

[0090] FIG. 6 is a diagram showing an example of tag information according to various embodiments.

[0091] Referring to FIG. 6, for example, a first user may connect to a web page corresponding to the service platform. For example, the first user who has logged in to the web page may connect to a web page for purchasing / selling a specific product. For example, the web page for purchasing / selling the specific product connected by the first user includes a photo of the specific product, the product name of the specific product, the selling price of the specific product, benefits (for details, reference may be made to the description of various embodiments described later with reference to FIG. 7), the origin of the product, the manufacturer of the product, an explanation of the product including the delivery method, etc., a user interface / user experience (UI / UX: user interface, user experience) for requesting the purchase of the product, a UI / UX for logging out, and a UI / UX for requesting the generation of product tags.

[0092] For example, based on a touch input of the first user or the like, a request for generating tag information may be distributed to the server via the UI / UX for requesting the generation of product tags. For example, the tag information may correspond to a Quick Response (QR) code. For example, the tag information may be generated in the form of a QR code, but various embodiments are not limited thereto.

[0093] According to various embodiments, the tag information may include product information, distribution information, and first user information.

[0094] According to various embodiments, the product information may include an explanation about the product. For example, the product information may include information corresponding to one or more of a photo of the product displayed on the web page, the product name, the product price, and the product description.

[0095] According to various embodiments, the distribution information may include information related to the distribution of fees associated with the sale of the product. For details, reference may be made to the description of various embodiments described later with reference to FIG. 7.

[0096] According to various embodiments, the first user information may include information related to a first user who requested generation of tag information (recommending a specific product recommended by the tag information).

[0097] For example, the first user may send / deliver the tag information to a second user. For example, for the sending / delivery of the tag information, it may include cases where the tag information is directly delivered / sent from the first user to the second user, and / or the tag information is delivered / sent from the first user to the second user via a third party, and / or the tag information is broadcast from the first user and the second user receives it, and / or the tag information is posted by the first user to an SNS, social media, other web pages, etc., and the second user views it, but is not limited thereto.

[0098] For example, the second user who has received the tag information may connect to a web page for purchasing / selling a specific product recommended by the first user through the tag information. For example, a web page for purchasing / selling a specific product connected by a second user who is not logged in may include a photo of the specific product, the product name of the specific product, the selling price of the specific product, benefits, the origin of the product, the manufacturer of the product, an explanation of the product including the delivery method, a UI / UX for a purchase request of the product, a UI / UX for membership registration, and a UI / UX for a request for generating a product tag.

[0099] For example, if the second user is a member of the service platform, the second user may log in via a login UI / UX (not shown).

[0100] For example, if the second user is not a member of / has not joined the service platform, the second user may request to newly join the service platform via the UI / UX for membership. For example, when a new membership request is made, a web page for joining the service platform may be displayed on the second user's user device. For example, the web page for joining the service platform may include a Recommender field. For example, based on the first user information included in the tag information, the ID (identifier) of the first user is identified, and the identified ID of the first user may be automatically input into the Recommender field.

[0101] FIG. 7 is a diagram showing an example of commission distribution according to various embodiments.

[0102] Referring to FIG. 7, in the description of various embodiments, the selling price may include the product price and the commission. For example, the selling price of a product sold on a service platform / web page may be the sum of the product price and the commission, but is not limited thereto.

[0103] According to various embodiments, the product price and the commission may be set by a seller who wishes to sell the product through the service platform and / or the web page.

[0104] According to various embodiments, the distribution information included in the tag information may include first distribution ratio information, second distribution ratio information, and third distribution ratio information. For example, each of the first distribution ratio information, the second distribution ratio information, and the third distribution ratio information may be set in percentages (%), and the sum of the first distribution ratio information, the second distribution ratio information, and the third distribution ratio information may be 100%, but is not limited thereto.

[0105] According to various embodiments, the first distribution ratio information may be information regarding the ratio at which the commission is distributed to the operator operating the service platform.

[0106] According to various embodiments, the second distribution ratio information may be information regarding a ratio at which a commission is distributed to a purchaser when a recipient of the tag information (the second user in FIG. 6) purchases a product through the tag information.

[0107] According to various embodiments, the third distribution ratio information may be information regarding a ratio at which a commission is distributed to a sender of the tag information (the first user in FIG. 6) when a recipient of the tag information (the second user in FIG. 6) purchases a product through the tag information.

[0108] For example, when a commission of 10,000 won occurs, the ratio according to the first distribution ratio information is 30%, the ratio according to the second distribution ratio information is 50%, and the ratio according to the third distribution ratio information is 20%, 3,000 won may be distributed to the operator, 5,000 won may be distributed to the purchaser, and 2,000 won may be distributed to the tag information sender. For example, the amount distributed to the purchaser and / or the tag information sender may be distributed in the form of mileage accumulation to the purchaser ID and / or the tag information sender ID.

[0109] According to various embodiments, the first distribution ratio information may be set by an operator operating the service platform. According to various embodiments, the second distribution ratio information and / or the third distribution ratio information may be set by a seller of the product.

[0110] According to various embodiments, the benefit described with reference to FIG. 6 may include the second distribution ratio information and / or the third distribution ratio information, and preferably may include the second distribution ratio information.

[0111] According to the various embodiments described above, a plurality of users may use a service platform according to the various embodiments, and big data may be configured based on a usage history of the service platform by the plurality of users. According to various embodiments, a service may be provided based on artificial intelligence generated by learning based on the configured big data. This will be described in more detail later.

[0112] 2.2 Services Based on Artificial Intelligence

[0113] An artificial intelligence system is a computer system that realizes human-level intelligence. Different from existing smart systems based on rules, it is a system in which machines learn, judge, and become wiser on their own. As the artificial intelligence system is used, the recognition rate improves, and it understands the user's preferences more accurately. Therefore, existing smart systems based on rules are gradually being replaced by artificial intelligence systems based on deep learning.

[0114] Artificial intelligence technology is composed of machine learning (deep learning) and enabling technologies that utilize machine learning.

[0115] Machine learning is an algorithm technology that classifies / learns the characteristics of input data by itself. Enabling technologies are technologies that utilize machine learning algorithms such as deep learning to imitate the functions of the human brain such as recognition and judgment, and are composed of technical fields such as language understanding, visual understanding, inference / prediction, knowledge representation, and motion control.

[0116] The various fields to which artificial intelligence technology is applied are as follows. Language understanding is a technology that recognizes, applies, and processes human language / characters, and includes natural language processing, machine translation, dialogue systems, question and answer, speech recognition / synthesis, etc. Visual understanding is a technology that recognizes and processes objects like human vision, and includes object recognition, object tracking, video (image) search, person recognition, scene understanding, spatial understanding, video (image) improvement, etc. Inference prediction is a technology that judges information and logically infers and predicts, and includes inference based on knowledge / probability, optimization prediction, plan based on preferences, recommendation, etc. Knowledge representation is a technology that automatically processes human experience information as knowledge data, and includes knowledge construction (data generation / classification), knowledge management (data utilization), etc. Motion control is a technology that controls the autonomous driving of vehicles and the movement of robots, and includes motion control (navigation, collision, driving), operation control (behavior control), etc.

[0117] In the following description, various embodiments will be described on the premise that the server device 100 executes an artificial intelligence engine acquisition operation. However, according to various embodiments, another server device external to the server device 100 may execute the artificial intelligence engine acquisition operation. Alternatively, according to various embodiments, a plurality of user devices 200 and / or a plurality of server devices 100 may be provided, and each operation of the artificial intelligence engine acquisition operation may be distributed and individually executed among the plurality of user devices 200 and / or the plurality of server devices 100.

[0118] FIG. 8 is a diagram showing a process of acquiring an artificial intelligence (AI) engine according to various embodiments.

[0119] Referring to FIG. 8, for example, the server device 100 may collect learning data. For example, the learning data may include information regarding the product recommendation history of each of the plurality of users, information regarding the purchase history of each of the plurality of users, information regarding the relevance between the information regarding the product recommendation history and the information regarding the purchase history, and profile information including the age, gender, email, mobile phone, and address of the plurality of users.

[0120] For example, the information regarding the relevance may include an evaluation index as to whether the purchase of a specific product by a specific third user is derived from a specific product recommendation.

[0121] For example, the evaluation index may be set based on the number of recipients who purchased the specific product among a plurality of recipients including the specific user who received the specific product recommendation, the weight value (weighted value) related to the time interval from the time when the recipient who purchased the specific product received the specific product recommendation to the time when the recipient who purchased the specific product made the purchase of the specific product, the type of the specific product, the profile information of the user who sent the specific product recommendation, the number of recipients who repurchased the specific product within a preset time from the time when the recipient who purchased the specific product made the purchase, the profile information of the recipients who repurchased, and the price of the specific product. For example, the weight value may be set such that it decreases as the length of the time interval increases and increases as the length of the time interval decreases.

[0122] For example, the learning data may include training data and test data. For example, the server device 100 may classify the learning data into training data and test data. For example, the training data may be utilized to train the model, and the test data may be utilized to verify and update the trained model.

[0123] For example, the server may train an artificial intelligence engine based on training data. For example, the artificial intelligence engine may be an artificial intelligence engine trained based on an artificial intelligence algorithm. For example, the artificial intelligence engine may include a deep neural network (DNN). For example, examples of the artificial intelligence engine include, but are not limited to, a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), an RBM (Restricted Boltzmann Machine), a DBN (Deep Belief Network), a bidirectional recurrent deep neural network (BRDNN), or a deep Q-network (Deep Q-Networks).

[0124] For example, the server device 100 may obtain processed data for test data based on a trained model.

[0125] For example, the server device 100 may obtain feedback information regarding the processed data.

[0126] For example, the feedback information may be input from the administrator of the server device 100.

[0127] As another example, as a response to the recommended product information, If one or more products sold on the service platform that are not included in the recommended product list are purchased from a specific second user, and none of the products included in the recommended product list are purchased, an operation of generating second feedback information including information on one or more products purchased from the specific second user, and an operation of updating the recommendation model based on the second feedback information may be performed.

[0128] For example, the server device 100 may update a learned model based on the feedback.

[0129] For example, the server device 100 may correct the characteristics of a learned model (for example, the characteristics of a CNN) based on the feedback, and repeat the process of obtaining and updating the feedback based on the corrected model.

[0130] For example, when the number of repetitions reaches a preset threshold value (for example, 7 to 9 times), the server device 100 may end the operation and obtain the learned model as an artificial intelligence engine.

[0131] FIG. 9 is a diagram showing an operation method of an electronic device for obtaining an artificial intelligence engine according to various embodiments.

[0132] Referring to FIG. 9, for example, in operation 901, the server device 100 may cause a model to be learned based on training data among the training data and test data obtained by processing learning data.

[0133] For example, in operation 903, the server device 100 may obtain first feedback information regarding the processed data output as a response to the input of the test data as a learned model.

[0134] For example, in operation 905, the server device 100 may update the model based on the first feedback information.

[0135] For example, in operation 907, the server device 100 may compare the number of times operations 901 to 905 are repeated with a preset threshold value (N_TH).

[0136] For example, if the number of times repeated does not match the preset threshold value (that is, if the number of times repeated is less than the preset threshold value), the server device 100 may return to operation 901 and execute operations 901 to 905 again.

[0137] For example, if the number of times repeated matches the preset threshold value, the server device 100 may end the process.

[0138] An example of the proposed method described above can also be included as one of various embodiments, so it is an obvious fact that it is regarded as a kind of proposed method. Also, the proposed method described above can be embodied independently, but can also be embodied in the form of a combination (or merger) of some proposed methods.

[0139] Various embodiments can be embodied as other specific forms without departing from the technical idea and essential features thereof. Therefore, the above detailed description should not be construed as restrictive in any way, but should be regarded as exemplary. The scope of various embodiments should be determined by a reasonable interpretation of the appended claims, and any changes within the equivalent scope of various embodiments are included in the scope of various embodiments. Also, embodiments can be constituted by combining claims that do not have an explicit citation relationship in the claims, or can be included as new claims by amendments after filing.

Claims

1. A memory, A processor connected to the memory, comprising The processor As a response to a request from a specific first user among a plurality of users who are members of a service platform operated based on an electronic device, generates tag information related to the recommendation of a product by the specific first user, Transmits the tag information to the specific first user, The tag information - is generated to satisfy at least one of the following: (i) being distributable to one or more of a first plurality of users excluding the specific first user among the plurality of users, or (ii) being distributable to one or more of a second plurality of users who are not members of the service platform, - An electronic device including product information including an explanation of the product, price information related to the product, distribution information related to the distribution of a commission associated with the sale of the product, and specific first user information related to the specific first user.

2. The price information includes information regarding the price of the product and information regarding the commission, The commission is set by the seller of the product, The selling price at which the product is sold on the service platform is set as the sum of the price of the product and the commission, The distribution information (i) First distribution ratio information regarding the ratio at which the commission is distributed to the operator operating the service platform, (ii) Second distribution ratio information regarding the ratio at which the commission is distributed to the purchaser when the purchaser of the product is included in the first plurality of users or the second plurality of users, (iii) When the purchaser is included in the first plurality of users or the second plurality of users, third allocation ratio information regarding a ratio at which the fee is allocated to the specific first user, and the electronic device according to claim 1, comprising:

3. The processor is When the purchaser is included in the first plurality of users or the second plurality of users, based on the allocation information included in the tag information, an amount calculated based on the fee and the second allocation ratio information is accumulated for the specific first user, and an amount calculated based on the fee and the third allocation ratio information is accumulated for the purchaser, and the electronic device according to claim 2, which is set to

4. The processor is As a response to a request of a specific second user among the plurality of users, a recommended product list based on an output of a recommendation model obtained as a response to an input of information related to the specific second user is obtained, The electronic device according to claim 1, which is set to transmit recommended product information including the recommended product list to the specific second user.

5. The recommendation model is preset based on machine learning applied to an artificial intelligence (AI) engine for obtaining the recommendation model, The machine learning is -(a) Training the artificial intelligence engine based on training data for obtaining the recommendation model, -(b) Obtaining first feedback information regarding processed data output as a response to an input of the artificial intelligence engine of test data for verifying the trained artificial intelligence engine, -(c) Updating the artificial intelligence engine based on the first feedback information, and -(d) The above (a) to (c) are repeated, and each time the above (a) to (c) are repeated, a count value with an initial value of 0 is incremented by 1, and the process ends based on the count value matching a preset count threshold. is executed based on The training data and the test data are obtained based on learning data for obtaining the recommendation model. The learning data includes information regarding the product recommendation history of each of the plurality of users, information regarding the purchase history of each of the plurality of users, information regarding the relevance between the information regarding the product recommendation history and the information regarding the purchase history, and profile information including the ages, genders, email addresses, mobile phones, and addresses of the plurality of users. The information regarding the relevance includes an evaluation index as to whether the purchase of a specific product by a specific third user is derived from a specific product recommendation. The evaluation index is based on the number of recipients who purchased the specific product among a plurality of recipients including the specific third user who received the specific product recommendation, a weight value related to the time interval from the time when the recipient who purchased the specific product received the specific product recommendation to the time when the recipient who purchased the specific product made the purchase of the specific product, the type of the specific product, the profile information of the user who sent the specific product recommendation, the number of recipients who repurchased the specific product within a preset time from the time when the recipient who purchased the specific product made the purchase of the specific product, the profile information of the recipients who repurchased the product, and the price of the specific product. The longer the length of the time interval, the smaller the weight value. The electronic device according to claim 4, wherein the shorter the length of the time interval, the larger the weight value.

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