KR102487754B1 - Method and apparatus for providing information on markeing costs using a neural network by a server - Google Patents

Abstract

Embodiments of the present invention relate to a method and an apparatus for allowing a server to provide information on marketing costs to a first terminal by using a neural network. According to an embodiment of the present invention, the method can comprise: a step of receiving information on an advertisement subject from the first terminal, in which the information includes the name of the advertisement subject, information on an advertisement target, information on an advertisement purpose, information on a field related to the advertisement subject, websites related to the advertisement subject, and social network service (SNS) channel of the advertisement subject; a step of determining the awareness based on values related to the advertisement subject for a preset first period, in which the values related to the advertisement subject include the number of times the name of the advertisement subject is searched, the number of visitors to the websites related to the advertisement subject, and the values related to the SNS channel of the advertisement subject; a step of determining marketing costs for the advertisement subject through a cost model using a neural network based on the awareness, information on the advertisement target, information on the advertisement purpose, and the field related to the advertisement subject; a step of determining an influencer list based on the marketing costs on the advertisement subject and information on the advertisement subject; a step of transmitting the information on the marketing costs on the advertisement subject and the influencer list to the first terminal; and a step of receiving an approval message including one or more influencers from the influencer list from the first terminal. Therefore, reasonable marketing costs can be determined.

Description

Method and apparatus for a server to provide information on marketing costs to a terminal using a neural network

Embodiments of the present disclosure relate to a technology for providing information on a marketing cost to a terminal, and to a technology in which a server provides information on a marketing cost to a terminal using a neural network.

As the number of Internet users increases, consumers who search for a brand they are interested in through a search engine, search the social network service (SNS) channel of the brand they are interested in, and add the SNS channel of the brand they are interested in to their own SNS channels. It is increasing.

In line with this trend, marketing companies need to objectively determine product awareness according to the search volume for the product and information on the product's SNS channel, and effectively reduce marketing costs based on awareness and various information about the product. need to decide

Meanwhile, in general, marketing companies are marketing their products through influencers familiar to consumers as well as celebrities. At this time, if an influencer in a field similar to the product is frequently exposed to consumers, it can rather give consumers a negative perception of the product.

Therefore, the server and / or device determines the awareness of the advertising product through objective data such as search volume, number of visitors to the website, and values related to SNS channels, and based on the awareness and various information about the advertising product, the neural network It may be necessary to determine the marketing cost through , and provide a marketing cost and a list of influencers according to the marketing cost to the terminal.

Embodiments of the present disclosure may provide a method and apparatus for a server to provide marketing cost information to a terminal using a neural network.

Technical tasks to be achieved in the embodiments are not limited to those mentioned above, and other technical tasks not mentioned will be considered by those skilled in the art from various embodiments to be described below. can

A method in which a server provides information on a marketing cost to a first terminal by using a neural network, according to an embodiment, includes receiving information on an advertisement target from the first terminal, and Information about is the name of the advertisement target, information on the advertisement target, information on the purpose of advertisement, information on the field related to the advertisement target, website related to the advertisement target, and SNS channel (social network) of the advertisement target. service), and determines the awareness based on values related to the advertisement target for a preset first period, wherein the values related to the advertisement target include the number of times the name of the advertisement target is searched, and the web page related to the advertisement target. It includes the number of visitors to the site and values related to the SNS channel of the advertisement target, and a neural network is generated based on the awareness, information on the advertisement target, information on the purpose of the advertisement, and information on fields related to the advertisement target. The marketing cost for the advertisement target is determined through the cost model used, an influencer list is determined based on the marketing cost for the advertisement target and the information on the advertisement target, and marketing for the advertisement target is determined. Transmitting information on marketing costs including costs and the list of influencers to the first terminal, and receiving an approval message including at least one influencer from the list of influencers from the first terminal can include

According to one embodiment, the recognition may be determined by the following equation.

In the above equation, a is the recognition, s is the number of times the name of the advertisement target is searched, s _d is a default value for the number of times the name of the advertisement target is searched, and s ₀ is the name of a plurality of advertisement targets. the average number of times, v is the number of visitors to the website related to the advertisement target, v _d is a default value for the number of visitors, and v ₀ is the average number of visitors to the website related to the plurality of advertisement targets , wherein i is the number of hits to the SNS channel of the advertisement target, i _d is a default value for the number of hits, i ₀ is the average number of hits to the SNS channels of the plurality of advertisement targets, and c is the number of hits to the advertisement target The number of times the SNS channel of the target is added, c _d is a default value for the number of times added, and c ₀ may be the average number of times the SNS channels of the plurality of advertisement targets are added.

For example, the plurality of advertisement targets may correspond to the same field as a field related to the advertisement target.

For example, the marketing cost for the advertisement target may be changed based on a difference between the awareness for the first preset period and the awareness for the second preset period.

For example, the preset first period may be a period before receiving the approval message from the first terminal. For example, the preset second period may be a period after receiving the approval message from the first terminal.

According to one embodiment, a plurality of first keywords for each of a plurality of pre-set influencers may be obtained through first web crawling. For example, a plurality of second keywords associated with the name of the advertisement target may be obtained through second web crawling. For example, among the plurality of preset influencers, a plurality of first influencers having the same first keyword as at least one of the plurality of second keywords may be determined. For example, a plurality of second influencers may be determined from among the plurality of first influencers. For example, the marketing cost for each of the plurality of second influencers may be equal to or less than a preset cost. For example, a degree of association with a field related to the advertisement target may be determined for each of the plurality of second influencers. For example, among the plurality of second influencers, a preset number of second influencers may be determined in descending order of relevance to a field related to the advertisement target. For example, the influencer list may include the preset number of second influencers.

According to an embodiment, the server may transmit a measurement message for measuring awareness of the advertisement target to a plurality of preset second terminals. For example, the server may receive a response message to the measurement message from at least one second terminal among the plurality of preset second terminals. For example, the awareness may be adjusted based on the number of response messages.

For example, the neural network may include an input layer, one or more hidden layers, and an output layer. For example, each learning data consisting of a plurality of awareness, information on a plurality of advertisement targets, information on a plurality of advertisement purposes, information on a field related to a plurality of advertisement targets, and correct marketing cost is It is input to an input layer, passes through the one or more hidden layers and an output layer, and is output as an output vector. The output vector is input to a loss function layer connected to the output layer, and the loss function layer is input to the output vector and each learning function. The cost model may be generated in which a loss value is output using a loss function that compares answer vectors for data, and parameters of the neural network are learned in a direction in which the loss value becomes smaller.

Additionally, for example, a marketing cost for an advertisement target may be changed by the following equation.

In the above equation, m _c is marketing cost for the changed advertisement target, a is awareness for the first preset period, a _m is awareness for the second preset period, and a _d is a default value for awareness, and m _t may be a marketing cost for the advertisement target. For example, a default value for the recognition may be preset in the server.

Additionally, for example, the preset cost may be determined by the following equation.

In the above equation, M _th is the preset cost, m _t is the marketing cost for the advertisement target, n is the number of the plurality of second influencers, and m _i is the i-th second A marketing cost for an influencer, a is the awareness, and a ₀ may be a preset awareness of a field related to the advertisement target. For example, the pre-set awareness may be set according to marketing costs for the advertisement target. For example, the higher the marketing cost for the advertisement target, the higher the preset awareness may be set.

According to embodiments, the server efficiently reduces marketing costs by determining awareness based on the number of times the name of an advertisement subject is searched, the number of visitors to a website related to the advertisement subject, and a value related to the SNS channel of the advertisement subject. You can create variables to determine. In addition, the server may derive the marketing cost through various variables that may affect the marketing cost by determining the marketing cost through a cost model using a neural network based on the recognition and the information about the advertisement target.

According to embodiments, the server may determine a reasonable marketing cost by adjusting the marketing cost by reflecting the difference between the awareness of the marketing period and the existing awareness.

According to embodiments, when providing an influencer list for marketing of the advertisement target, the server provides an influencer candidate having a low relationship with the field of the advertisement target within the marketing cost, thereby providing an interest in the advertisement target. can cause

Effects obtainable from the embodiments are not limited to the effects mentioned above, and other effects not mentioned are clearly derived and understood by those skilled in the art based on the detailed description below. It can be.

BRIEF DESCRIPTION OF THE DRAWINGS Included as part of the detailed description to aid understanding of the embodiments, the accompanying drawings provide various embodiments and, together with the detailed description, describe technical features of the various embodiments.
1 is a diagram illustrating a configuration of an electronic device according to an exemplary embodiment.
2 is a diagram showing the configuration of a program according to an embodiment.
3 illustrates a method in which a server provides marketing cost information to a first terminal according to an exemplary embodiment.
4 is an example of a procedure for determining an influencer list according to an embodiment.
5 shows an example of a cost model according to one embodiment.
6 is a block diagram illustrating a configuration of a server according to an exemplary embodiment.

The following embodiments combine elements and features of the embodiments in a predetermined form. Each component or feature may be considered optional unless explicitly stated otherwise. Each component or feature may be implemented in a form not combined with other components or features. In addition, various embodiments may be configured by combining some components and/or features. The order of operations described in various embodiments may be changed. Some components or features of one embodiment may be included in another embodiment, or may be replaced with corresponding components or features of another embodiment.

In the description of the drawings, procedures or steps that may obscure the gist of various embodiments are not described, and procedures or steps that can be understood by those skilled in the art are not described. did

Throughout the specification, when a part is said to "comprising" or "including" a certain element, it means that it may further include other elements, not excluding other elements, unless otherwise stated. do. In addition, terms such as “… unit”, “… unit”, and “module” described in the specification mean a unit that processes at least one function or operation, which is hardware or software or a combination of hardware and software. can be implemented as Also, “a or an”, “one”, “the” and like terms are used herein in the context of describing various embodiments (particularly in the context of the claims below). Unless otherwise indicated or clearly contradicted by context, both the singular and the plural can be used.

Hereinafter, embodiments according to various embodiments will be described in detail with reference to the accompanying drawings. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The detailed description set forth below in conjunction with the accompanying drawings is intended to describe exemplary embodiments of various embodiments, and is not intended to represent a single embodiment.

In addition, specific terms used in various embodiments are provided to help understanding of various embodiments, and the use of these specific terms may be changed into other forms without departing from the technical spirit of various embodiments. .

1 is a diagram illustrating a configuration of an electronic device according to an exemplary embodiment.

1 is a block diagram of an electronic device 101 within a network environment 100, according to various embodiments. Referring to FIG. 1 , in a network environment 100, an electronic device 101 communicates with an electronic device 102 through a first network 198 (eg, a short-range wireless communication network) or through a second network 199. It may communicate with at least one of the electronic device 104 or the server 108 through (eg, a long-distance wireless communication network). According to one embodiment, the electronic device 101 may communicate with the electronic device 104 through the server 108 . According to an embodiment, the electronic device 101 includes a processor 120, a memory 130, an input module 150, an audio output module 155, a display module 160, an audio module 170, a sensor module ( 176), interface 177, connection terminal 178, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196 , or the antenna module 197 may be included. In some embodiments, in the electronic device 101, at least one of these components (eg, the connection terminal 178) may be omitted or one or more other components may be added. In some embodiments, some of these components (eg, sensor module 176, camera module 180, or antenna module 197) are integrated into a single component (eg, display module 160). It can be. The electronic device 101 may also be referred to as a client, terminal, or peer.

The processor 120, for example, executes software (eg, the program 140) to cause at least one other component (eg, hardware or software component) of the electronic device 101 connected to the processor 120. It can control and perform various data processing or calculations. According to one embodiment, as at least part of data processing or operation, the processor 120 transfers instructions or data received from other components (e.g., sensor module 176 or communication module 190) to volatile memory 132. , processing commands or data stored in the volatile memory 132 , and storing resultant data in the non-volatile memory 134 . According to one embodiment, the processor 120 may include a main processor 121 (eg, a central processing unit or an application processor) or a secondary processor 123 (eg, a graphic processing unit, a neural network processing unit ( NPU: neural processing unit (NPU), image signal processor, sensor hub processor, or communication processor). For example, when the electronic device 101 includes the main processor 121 and the auxiliary processor 123, the auxiliary processor 123 may use less power than the main processor 121 or be set to be specialized for a designated function. can The secondary processor 123 may be implemented separately from or as part of the main processor 121 .

The secondary processor 123 may, for example, take the place of the main processor 121 while the main processor 121 is in an inactive (eg, sleep) state, or the main processor 121 is active (eg, running an application). ) state, together with the main processor 121, at least one of the components of the electronic device 101 (eg, the display module 160, the sensor module 176, or the communication module 190) It is possible to control at least some of the related functions or states. According to one embodiment, the auxiliary processor 123 (eg, image signal processor or communication processor) may be implemented as part of other functionally related components (eg, camera module 180 or communication module 190). there is. According to an embodiment, the auxiliary processor 123 (eg, a neural network processing device) may include a hardware structure specialized for processing an artificial intelligence model.

AI models can be created through machine learning. Such learning may be performed, for example, in the electronic device 101 itself where the artificial intelligence model is performed, or may be performed through a separate server (eg, the server 108). The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning or reinforcement learning, but in the above example Not limited. The artificial intelligence model may include a plurality of artificial neural network layers. Artificial neural networks include deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), restricted boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recurrent deep neural networks (BRDNNs), It may be one of deep Q-networks or a combination of two or more of the foregoing, but is not limited to the foregoing examples. The artificial intelligence model may include, in addition or alternatively, software structures in addition to hardware structures.

The memory 130 may store various data used by at least one component (eg, the processor 120 or the sensor module 176) of the electronic device 101 . The data may include, for example, input data or output data for software (eg, program 140) and commands related thereto. The memory 130 may include volatile memory 132 or non-volatile memory 134 .

The program 140 may be stored as software in the memory 130 and may include, for example, an operating system 142 , middleware 144 , or an application 146 .

The input module 150 may receive a command or data to be used by a component (eg, the processor 120) of the electronic device 101 from the outside of the electronic device 101 (eg, a user). The input module 150 may include, for example, a microphone, a mouse, a keyboard, a key (eg, a button), or a digital pen (eg, a stylus pen).

The sound output module 155 may output sound signals to the outside of the electronic device 101 . The sound output module 155 may include, for example, a speaker or a receiver. The speaker can be used for general purposes such as multimedia playback or recording playback. A receiver may be used to receive an incoming call. According to one embodiment, the receiver may be implemented separately from the speaker or as part of it.

The display module 160 may visually provide information to the outside of the electronic device 101 (eg, a user). The display module 160 may include, for example, a display, a hologram device, or a projector and a control circuit for controlling the device. According to one embodiment, the display module 160 may include a touch sensor set to detect a touch or a pressure sensor set to measure the intensity of force generated by the touch.

The audio module 170 may convert sound into an electrical signal or vice versa. According to one embodiment, the audio module 170 acquires sound through the input module 150, the sound output module 155, or an external electronic device connected directly or wirelessly to the electronic device 101 (eg: Sound may be output through the electronic device 102 (eg, a speaker or a headphone).

The sensor module 176 detects an operating state (eg, power or temperature) of the electronic device 101 or an external environmental state (eg, a user state), and generates an electrical signal or data value corresponding to the detected state. can do. According to one embodiment, the sensor module 176 may include, for example, a gesture sensor, a gyro sensor, an air pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a bio sensor, It may include a temperature sensor, humidity sensor, or light sensor.

The interface 177 may support one or more designated protocols that may be used to directly or wirelessly connect the electronic device 101 to an external electronic device (eg, the electronic device 102). According to one embodiment, the interface 177 may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

The connection terminal 178 may include a connector through which the electronic device 101 may be physically connected to an external electronic device (eg, the electronic device 102). According to one embodiment, the connection terminal 178 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (eg, a headphone connector).

The haptic module 179 may convert electrical signals into mechanical stimuli (eg, vibration or motion) or electrical stimuli that a user may perceive through tactile or kinesthetic senses. According to one embodiment, the haptic module 179 may include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

The camera module 180 may capture still images and moving images. According to one embodiment, the camera module 180 may include one or more lenses, image sensors, image signal processors, or flashes.

The power management module 188 may manage power supplied to the electronic device 101 . According to one embodiment, the power management module 188 may be implemented as at least part of a power management integrated circuit (PMIC), for example.

The battery 189 may supply power to at least one component of the electronic device 101 . According to one embodiment, the battery 189 may include, for example, a non-rechargeable primary cell, a rechargeable secondary cell, or a fuel cell.

The communication module 190 is a direct (eg, wired) communication channel or a wireless communication channel between the electronic device 101 and an external electronic device (eg, the electronic device 102, the electronic device 104, or the server 108). Establishment and communication through the established communication channel may be supported. The communication module 190 may include one or more communication processors that operate independently of the processor 120 (eg, an application processor) and support direct (eg, wired) communication or wireless communication. According to one embodiment, the communication module 190 is a wireless communication module 192 (eg, a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module 194 (eg, : a local area network (LAN) communication module or a power line communication module). Among these communication modules, a corresponding communication module is a first network 198 (eg, a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network 199 (eg, legacy It may communicate with the external electronic device 104 through a cellular network, a 5G network, a next-generation communication network, the Internet, or a telecommunications network such as a computer network (eg, a LAN or a WAN). These various types of communication modules may be integrated as one component (eg, a single chip) or implemented as a plurality of separate components (eg, multiple chips). The wireless communication module 192 uses subscriber information (eg, International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module 196 within a communication network such as the first network 198 or the second network 199. The electronic device 101 may be identified or authenticated.

The wireless communication module 192 may support a 5G network after a 4G network and a next-generation communication technology, for example, NR access technology (new radio access technology). NR access technologies include high-speed transmission of high-capacity data (enhanced mobile broadband (eMBB)), minimization of terminal power and access of multiple terminals (massive machine type communications (mMTC)), or high reliability and low latency (ultra-reliable and low latency (URLLC)). -latency communications)) can be supported. The wireless communication module 192 may support a high frequency band (eg, mmWave band) to achieve a high data rate, for example. The wireless communication module 192 uses various technologies for securing performance in a high frequency band, such as beamforming, massive multiple-input and multiple-output (MIMO), and full-dimensional multiplexing. Technologies such as input/output (FD-MIMO: full dimensional MIMO), array antenna, analog beam-forming, or large scale antenna may be supported. The wireless communication module 192 may support various requirements defined for the electronic device 101, an external electronic device (eg, the electronic device 104), or a network system (eg, the second network 199). According to one embodiment, the wireless communication module 192 is a peak data rate for eMBB realization (eg, 20 Gbps or more), a loss coverage for mMTC realization (eg, 164 dB or less), or a U-plane latency for URLLC realization (eg, Example: downlink (DL) and uplink (UL) each of 0.5 ms or less, or round trip 1 ms or less) may be supported.

The antenna module 197 may transmit or receive signals or power to the outside (eg, an external electronic device). According to one embodiment, the antenna module 197 may include an antenna including a radiator formed of a conductor or a conductive pattern formed on a substrate (eg, PCB). According to one embodiment, the antenna module 197 may include a plurality of antennas (eg, an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network such as the first network 198 or the second network 199 is selected from the plurality of antennas by the communication module 190, for example. can be chosen A signal or power may be transmitted or received between the communication module 190 and an external electronic device through the selected at least one antenna. According to some embodiments, other components (eg, a radio frequency integrated circuit (RFIC)) may be additionally formed as a part of the antenna module 197 in addition to the radiator.

According to various embodiments, the antenna module 197 may form a mmWave antenna module. According to one embodiment, the mmWave antenna module includes a printed circuit board, an RFIC disposed on or adjacent to a first surface (eg, a lower surface) of the printed circuit board and capable of supporting a designated high frequency band (eg, mmWave band); and a plurality of antennas (eg, array antennas) disposed on or adjacent to a second surface (eg, a top surface or a side surface) of the printed circuit board and capable of transmitting or receiving signals of the designated high frequency band. can do.

At least some of the components are connected to each other through a communication method between peripheral devices (eg, a bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)) and signal ( e.g. commands or data) can be exchanged with each other.

According to an embodiment, commands or data may be transmitted or received between the electronic device 101 and the external electronic device 104 through the server 108 connected to the second network 199 . Each of the external electronic devices 102 or 104 may be the same as or different from the electronic device 101 . According to an embodiment, all or part of operations executed in the electronic device 101 may be executed in one or more external electronic devices among the external electronic devices 102 , 104 , or 108 . For example, when the electronic device 101 needs to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device 101 instead of executing the function or service by itself. Alternatively or additionally, one or more external electronic devices may be requested to perform the function or at least part of the service. One or more external electronic devices receiving the request may execute at least a part of the requested function or service or an additional function or service related to the request, and deliver the execution result to the electronic device 101 . The electronic device 101 may provide the result as at least part of a response to the request as it is or additionally processed. To this end, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used. The electronic device 101 may provide an ultra-low latency service using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device 104 may include an internet of things (IoT) device. Server 108 may be an intelligent server using machine learning and/or neural networks. According to one embodiment, the external electronic device 104 or server 108 may be included in the second network 199 . The electronic device 101 may be applied to intelligent services (eg, smart home, smart city, smart car, or health care) based on 5G communication technology and IoT-related technology.

The server 108 is connected to the electronic device 101 and can provide a service to the connected electronic device 101 . In addition, the server 108 may proceed with a membership sign-up procedure, store and manage various types of information of users subscribed as members, and provide various purchase and payment functions related to services. In addition, the server 108 may share execution data of service applications executed in each of the plurality of electronic devices 101 in real time so that the service can be shared among users. This server 108 may have the same configuration as a conventional web server or service server in terms of hardware. However, in terms of software, it may include a program module that is implemented through any language such as C, C++, Java, Python, Golang, or kotlin and performs various functions. In addition, the server 108 is generally connected to an unspecified number of clients and/or other servers through an open computer network such as the Internet, and receives requests from clients or other servers to perform tasks and derives and provides work results. It means a computer system and the computer software (server program) installed for it. In addition, the server 108, in addition to the above-described server program, a series of application programs operating on the server 108 and various databases (DB: Database, hereinafter referred to as It should be understood as a broad concept including DB"). Accordingly, the server 108 classifies member registration information and various information and data about games, stores them in a DB, and manages them. This DB may be implemented inside or outside the server 108 . In addition, the server 108 may be implemented using server programs that are provided in various ways according to operating systems such as Windows, Linux, UNIX, and Macintosh on general server hardware, As a representative example, a web service can be implemented using IIS (Internet Information Server) used in a Windows environment and CERN, NCSA, APPACH, TOMCAT, etc. used in a Unix environment. In addition, the server 108 may interoperate with an authentication system and a payment system for user authentication of services or payment for purchases related to services.

The first network 198 and the second network 199 are a connection structure capable of exchanging information between nodes such as terminals and servers, or a network connecting the server 108 and the electronic devices 101 and 104. means (Network). The first network 198 and the second network 199 include Internet, Local Area Network (LAN), Wireless Local Area Network (Wireless Local Area Network), Wide Area Network (WAN), Personal Area Network (PAN), and 3G , 4G, LTE, 5G, Wi-Fi, etc., but are not limited thereto. The first network 198 and the second network 199 may be closed first networks 198 and second networks 199 such as LAN and WAN, but are preferably open such as the Internet. The Internet includes protocols such as the TCP/IP protocol, TCP, and User Datagram Protocol (UDP), and various services that exist in the upper layer, such as HTTP (HyperText Transfer Protocol), Telnet, FTP (File Transfer Protocol), and DNS (Domain Name System). ), Simple Mail Transfer Protocol (SMTP), Simple Network Management Protocol (SNMP), Network File Service (NFS), and Network Information Service (NIS). ) structure.

A database may have a general data structure implemented in a storage space (hard disk or memory) of a computer system using a database management program (DBMS). The database may have a data storage form in which data can be freely searched for (extracted), deleted, edited, added, and the like. Databases are relational database management systems (RDBMS) such as Oracle, Informix, Sybase, and DB2, or object-oriented database management such as Gemston, Orion, and O2. It can be implemented according to the purpose of an embodiment of the present disclosure using a system (OODBMS) and XML Native Databases such as Excelon, Tamino, and Sekaiju, and its functions may have appropriate fields or elements to achieve.

2 is a diagram showing the configuration of a program according to an embodiment.

2 is a block diagram 200 illustrating a program 140 according to various embodiments. According to one embodiment, the program 140 includes an operating system 142, middleware 144, or an application 146 executable in the operating system 142 for controlling one or more resources of the electronic device 101. can include The operating system 142 may include, for example, Android™, iOS™, Windows™, Symbian™, Tizen™, or Bada™. At least some of the programs 140 are, for example, preloaded in the electronic device 101 at the time of manufacture, or when used by a user, an external electronic device (eg, the electronic device 102 or 104), or a server ( 108)) can be downloaded or updated. All or part of program 140 may include a neural network.

The operating system 142 may control management (eg, allocation or reclamation) of one or more system resources (eg, process, memory, or power) of the electronic device 101 . Operating system 142 may additionally or alternatively include other hardware devices of electronic device 101 , such as input module 150 , sound output module 155 , display module 160 , audio module 170 . , sensor module 176, interface 177, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196, or It may include one or more driver programs for driving the antenna module 197.

The middleware 144 may provide various functions to the application 146 so that the function or information provided from one or more resources of the electronic device 101 may be used by the application 146 . The middleware 144 includes, for example, the application manager 201, the window manager 203, the multimedia manager 205, the resource manager 207, the power manager 209, the database manager 211, and the package manager 213. ), connectivity manager 215, notification manager 217, location manager 219, graphics manager 221, security manager 223, call manager 225, or voice recognition manager 227. can

The application manager 201 may manage the life cycle of the application 146 , for example. The window manager 203 may manage one or more GUI resources used in a screen, for example. The multimedia manager 205 identifies, for example, one or more formats necessary for reproducing media files, and encodes or decodes a corresponding media file among the media files using a codec suitable for the selected format. can be done The resource manager 207 may manage a source code of the application 146 or a memory space of the memory 130 . The power manager 209 may manage, for example, the capacity, temperature, or power of the battery 189, and determine or provide related information necessary for the operation of the electronic device 101 by using corresponding information among them. . According to an embodiment, the power manager 209 may interoperate with a basic input/output system (BIOS) (not shown) of the electronic device 101 .

The database manager 211 may create, search, or change a database to be used by the application 146, for example. The package manager 213 may manage installation or update of applications distributed in the form of package files, for example. The connectivity manager 215 may manage, for example, a wireless connection or a direct connection between the electronic device 101 and an external electronic device. The notification manager 217 may provide a function for notifying a user of occurrence of a designated event (eg, an incoming call, message, or alarm), for example. The location manager 219 may manage location information of the electronic device 101, for example. The graphic manager 221 may manage, for example, one or more graphic effects to be provided to a user or a user interface related thereto.

Security manager 223 may provide system security or user authentication, for example. The telephony manager 225 may manage, for example, a voice call function or a video call function provided by the electronic device 101 . The voice recognition manager 227 transmits, for example, the user's voice data to the server 108, and at least partially based on the voice data, a command corresponding to a function to be performed in the electronic device 101; Alternatively, text data converted at least partially based on the voice data may be received from the server 108 . According to one embodiment, the middleware 244 may dynamically delete some existing components or add new components. According to one embodiment, at least part of the middleware 144 may be included as part of the operating system 142 or may be implemented as separate software different from the operating system 142 .

The application 146 includes, for example, a home 251, a dialer 253, an SMS/MMS 255, an instant message (IM) 257, a browser 259, a camera 261, and an alarm 263. , Contacts (265), Voice Recognition (267), Email (269), Calendar (271), Media Player (273), Albums (275), Watch (277), Health (279) (e.g. exercise or blood sugar) measurement of biometric information) or environmental information 281 (eg, measurement of atmospheric pressure, humidity, or temperature information). According to an embodiment, the application 146 may further include an information exchange application (not shown) capable of supporting information exchange between the electronic device 101 and an external electronic device. The information exchange application may include, for example, a notification relay application configured to transmit designated information (eg, a call, message, or alarm) to an external electronic device, or a device management application configured to manage an external electronic device. there is. The notification relay application, for example, transmits notification information corresponding to a designated event (eg, mail reception) generated in another application (eg, the email application 269) of the electronic device 101 to an external electronic device. can Additionally or alternatively, the notification relay application may receive notification information from an external electronic device and provide the notification information to the user of the electronic device 101 .

The device management application is, for example, a power source (eg, turn-on or turn-off) of an external electronic device that communicates with the electronic device 101 or some component thereof (eg, a display module or a camera module of the external electronic device). ) or functions (eg brightness, resolution, or focus). The device management application may additionally or alternatively support installation, deletion, or update of an application operating in an external electronic device.

Throughout this specification, a neural network, a neural network, and a network function may be used interchangeably. A neural network may consist of a set of interconnected computational units, which may be generally referred to as “nodes”. These “nodes” may also be referred to as “neurons”. A neural network includes at least two or more nodes. Nodes (or neurons) constituting neural networks may be interconnected by one or more “links”.

In a neural network, two or more nodes connected through a link may form a relative relationship of an input node and an output node. The concept of an input node and an output node is relative, and any node in an output node relationship with one node may have an input node relationship with another node, and vice versa. As described above, the input node to output node relationship can be created around the link. More than one output node can be connected to one input node through a link, and vice versa.

In a relationship between an input node and an output node connected through one link, the value of the output node may be determined based on data input to the input node. Here, a node interconnecting the input node and the output node may have a weight. The weight may be variable, and may be changed by a user or an algorithm in order for the neural network to perform a desired function. For example, when one or more input nodes are interconnected by respective links to one output node, the output node is set to a link corresponding to values input to input nodes connected to the output node and respective input nodes. An output node value may be determined based on the weight.

As described above, in a neural network, two or more nodes are interconnected through one or more links to form an input node and output node relationship in the neural network. Characteristics of the neural network may be determined according to the number of nodes and links in the neural network, an association between the nodes and links, and a weight value assigned to each link. For example, when there are two neural networks having the same number of nodes and links and different weight values between the links, the two neural networks may be recognized as different from each other.

3 illustrates a method in which a server provides marketing cost information to a first terminal according to an exemplary embodiment. One embodiment of FIG. 3 may be combined with various embodiments of the present disclosure.

Referring to FIG. 3 , in step S301, the server may receive information on an advertisement target from the first terminal. For example, the information on the advertisement target includes the name of the advertisement target, information on the advertisement target, information on the purpose of the advertisement, information on a field related to the advertisement target, and a website related to the advertisement target and the advertisement It may include the SNS channel (social network service) of the target.

Here, the advertisement target may include at least one of a product to be advertised, a service to be advertised, and a brand to be advertised.

Here, the information on the advertisement target may be information on a target to which an advertisement is exposed. For example, the advertisement target information may include age group information, region information, and gender information.

Here, the information about the purpose of advertisement may be information about the purpose of advertisement. For example, the information on the advertisement purpose may include at least one of a plurality of preset first categories. For example, the information about the advertisement purpose may include at least one of sales increase, awareness increase, corporate promotion, product promotion, and event participation. For example, when the advertisement purpose is to raise awareness, information about the advertisement purpose may include a value matched to the awareness increase.

Here, the information on the field related to the advertisement target may be information on the field including the target to be advertised. For example, information about fields related to advertisement targets may include at least one of a plurality of preset second categories. For example, information on fields related to advertisement targets may include at least one of medical, health, beauty, fashion, household goods, interior, infants, home appliances, finance, and others. For example, if the field related to the advertisement target is fashion, information on the field related to the advertisement target may include a value matching the fashion.

Additionally, for example, the plurality of preset second categories may be periodically set based on fields related to advertisement targets collected through web crawling. Here, web crawling may be an operation of searching a plurality of web sites and collecting information within the searched web pages. For example, the server may collect information on categories for classifying products for sale from a plurality of e-commerce websites, and may collect information on categories for classifying products for sale at a predetermined period (eg, 1 years), the plurality of preset second categories may be set.

In step S302, the server may determine awareness based on values related to the advertisement target for a preset first period. For example, the values related to the advertisement subject may include the number of times the name of the advertisement subject is searched, the number of visitors to a website related to the advertisement subject, and a value related to the SNS channel of the advertisement subject. For example, the server may obtain values related to the advertisement target through a web log and an activity log for SNS. For example, the value related to the SNS channel of the advertisement target may be the number of hits to the SNS channel of the advertisement target and the number of times the SNS channel of the advertisement target is added.

For example, the recognition may be determined by Equation 1 below.

In Equation 1, a is the recognition, s is the number of times the name of the advertisement target is searched, s _d is a default value for the number of searches, and s ₀ is the name of a plurality of advertisement targets is the average number of searches, v is the number of visitors to the website related to the advertisement target, v _d is the default value for the number of visitors, and v ₀ is the average number of visitors to the website related to the plurality of advertisement targets. number, i is the number of hits to the SNS channel of the advertisement target, i _d is a default value for the number of hits, i ₀ is the average number of hits to the SNS channels of the plurality of advertisement targets, and c is The number of times the SNS channels of the advertisement target are added, c _d is a default value for the number of additions, and c ₀ may be the average number of times the SNS channels of the plurality of advertisement targets are added. For example, the recognition may have a value greater than or equal to 1 due to the max function.

For example, the plurality of advertisement targets may be a plurality of advertisement targets corresponding to the same field as the field related to the advertisement target. For example, if the field related to the advertisement target is fashion, a plurality of advertisement targets corresponding to the fashion may be set by a preset number through web crawling. For example, the server may determine s ₀ , v ₀ , i ₀ , and c ₀ through a web log and an SNS activity log for each of the plurality of advertisement targets. For this reason, the server may determine awareness considering characteristics of each field by using average values of a plurality of advertisement targets in the same field.

For example, the default values may be preset values in the server. As a result, it is possible to determine the awareness of an advertising target for which there is no web log or SNS activity log related to marketing.'

According to an embodiment, the server may transmit a measurement message for measuring awareness of the advertisement target to a plurality of preset second terminals. Here, the measurement message may include the name of the advertisement target. For example, the server may receive a response message to the measurement message from at least one second terminal among the plurality of preset second terminals. For example, the reply message may include a value indicating that the name of the advertisement target is recognized. For example, the awareness may be adjusted based on the response message. Additionally, for example, if the number of response messages is greater than or equal to a preset number, the server may add a weight according to the number of response messages to the recognition. For example, if the ratio of the at least one second terminal to the preset number of the plurality of second terminals is equal to or greater than the preset ratio, the server determines the at least one second terminal to the preset number of the plurality of second terminals. A weight according to the ratio of the second terminal may be added to the recognition. Due to this, it is possible to more accurately determine the recognition level.

In step S303, the server sets the marketing cost for the advertisement target through a cost model using a neural network based on the recognition, the information on the advertisement target, the information on the advertisement purpose, and the information on the field related to the advertisement target. can determine

For example, the neural network may include an input layer, one or more hidden layers, and an output layer. For example, each learning data consisting of a plurality of awareness, information on a plurality of advertisement targets, information on a plurality of advertisement purposes, information on a field related to a plurality of advertisement targets, and correct marketing cost is It is input to an input layer, passes through the one or more hidden layers and an output layer, and is output as an output vector. The output vector is input to a loss function layer connected to the output layer, and the loss function layer is input to the output vector and each learning function. The cost model may be generated in which a loss value is output using a loss function that compares answer vectors for data, and parameters of the neural network are learned in a direction in which the loss value becomes smaller.

For example, information on a plurality of recognition, information on a plurality of advertisement targets, information on a plurality of advertisement purposes, information on fields related to a plurality of advertisement targets, and correct marketing costs may be information stored in the server in the past.

For example, the marketing cost for the advertisement target may be changed based on a difference between awareness for the first preset period and awareness for the second preset period. For example, the preset first period may be a period before receiving the approval message from the first terminal. For example, the preset second period may be a period after receiving the approval message from the first terminal. Additionally, for example, the server may set a marketing period for the advertisement target based on the approval message. For example, the preset second period may include a marketing period for the advertisement target. For example, after the marketing period set for the advertisement target expires, the server calculates the marketing cost for the advertisement target based on the difference between awareness for the first preset period and awareness for the second preset period. can be changed For example, the server may transmit a message about end of marketing including a marketing cost for a changed advertisement target to the first terminal.

Additionally, for example, a marketing cost for an advertisement target may be changed by Equation 2 below.

In Equation 2, m _c is the marketing cost for the changed advertisement target, a is awareness for the first preset period, a _m is awareness for the second preset period, a _d is a default value for awareness, and m _t may be a marketing cost for the advertisement target. For example, a default value for the recognition may be preset in the server. For this reason, a reasonable marketing cost can be determined by adjusting the marketing cost according to the change in awareness of the advertising target.

In step S304, the server may determine an influencer list based on marketing costs for the advertisement target and information on the advertisement target. Here, an influencer means a person who has a great influence on society, and especially means a person on the web. For example, it can be referred to as influencer marketing when a company utilizes and promotes information delivered by an influencer.

For example, a plurality of first keywords for each of a plurality of preset influencers may be acquired through first web crawling. For example, a plurality of second keywords associated with the name of the advertisement target may be obtained through second web crawling. For example, among the plurality of preset influencers, a plurality of first influencers having the same first keyword as at least one of the plurality of second keywords may be determined. Here, marketing expenses for each of the plurality of preset influencers may be pre-stored in the server.

For example, a plurality of second influencers may be determined from among the plurality of first influencers. For example, the marketing cost for the second influencer may be equal to or less than a preset cost. That is, for example, the server may determine an influencer whose marketing cost for the influencer is equal to or less than a preset cost among the plurality of first influencers as the second influencer. For example, the preset cost may be determined by Equation 3 below.

In Equation 3, M _th is the preset cost, m _t is the marketing cost for the advertisement target, n is the number of the plurality of second influencers, and m _i is the i-th Marketing cost for the second influencer, a is the awareness, and a ₀ may be a preset awareness of a field related to the advertisement target. For example, the pre-set awareness may be set according to marketing costs for the advertisement target. For example, the higher the marketing cost for the advertisement target, the higher the preset awareness may be set. Due to this, it is possible to determine appropriate influencer candidates within the marketing cost by considering the average cost of the influencer and the awareness of the advertisement target.

For example, a degree of association with a field related to the advertisement target may be determined for each of the plurality of second influencers.

Additionally, for example, the server may determine a degree of similarity between fields related to an advertisement subject based on information on a plurality of advertisement subjects. For example, the average value of the gender of the advertisement target and the age group of the advertisement target may be determined for each of the fields related to the advertisement target, and the degree of similarity between the fields may be determined according to the difference between the average values of the respective fields. .

For example, fields related to advertisement targets may be set to medical, health, sports, beauty, fashion, household goods, interior, infants, home appliances, finance, and others. For example, when the field related to the advertisement target is medical, the server may determine an average value for the sexes of the plurality of advertisement targets corresponding to the medical field and the age groups of the plurality of advertisement targets. For example, for the medical field, the server sets the gender of the advertisement target to values of 1, 2, and 3 matching male, female, and male and female, and the age group of the advertising target to 1 matching 10s to 70s and all age groups. A plurality of first vector values composed of 8 to 8 values may be determined. For example, the server may determine a first average vector value of the plurality of first vector values as a value for the medical field. Also, for example, for a field of sports, the server may determine a plurality of second vector values, and may determine a second mean vector value for the plurality of second vector values as a value for the field of sports. For example, the server may determine the degree of similarity between the medical field and the sports field according to a Euclidean distance value between the first mean vector value and the second mean vector value. Also, for example, for each of the plurality of second influencers, a field related to an advertisement target may be set in advance.

For example, when the field related to the advertisement target is fashion, a Euclidean distance value between a field value for each of the plurality of second influencers and a value for the fashion field may be determined as a degree of association. That is, the degree of association decreases as the difference between the Euclidean distance values increases, and may increase as the Euclidean distance values decrease.

For example, if the field related to the advertisement target is fashion and the fields of each of the plurality of second influencers are health, beauty, and sports, the value for the fashion is [2.4, 5.1], and the value for the health If the values are [1.7, 4.8], the values for beauty are [2.6, 4.9], and the values for sports are [1.6, 3.6], the Euclidean distance between the values for fashion and the values for sports value may be the largest. Accordingly, the second influencer corresponding to the sports field may have the lowest correlation with the field related to the advertisement target.

For example, among the plurality of second influencers, a preset number of second influencers may be determined in descending order of relevance to a field related to the advertisement target. For example, the influencer list may include the preset number of second influencers.

In step S305, the server may transmit marketing cost information for the advertisement target and marketing cost information including the influencer list to the first terminal.

In step S306, the server may receive an approval message including at least one influencer from among the influencer list from the first terminal.

4 is an example of a procedure for determining an influencer list according to an embodiment. The embodiment of FIG. 4 can be combined with various embodiments of the present disclosure.

Referring to FIG. 4 , in step S401, the server may acquire a plurality of first keywords for each of a plurality of influencers preset through web crawling and a plurality of second keywords associated with the name of an advertisement target. For example, the plurality of first keywords may be top three keywords with the highest frequency among keywords associated with influencers. For example, the plurality of second keywords may be top three keywords with the highest frequency among keywords associated with the name of an advertisement target. For example, the plurality of second keywords may be "refined", "luxury", and "clean".

In step S402, the server may determine whether at least one of a plurality of second keywords is identical to a plurality of first keywords for each of a plurality of preset influencers. In step S403, the server may determine an influencer having a first keyword identical to at least one of a plurality of second keywords as a first influencer. For example, when a plurality of second keywords associated with a name for an advertisement subject are "refined", "luxury", and "clean", the server sets at least one of "refined", "luxury", or "clean" as a first keyword. An influencer with a keyword can be determined as the first influencer. At this time, the number of first influencers may be multiple.

In step S404, the server may determine whether a marketing cost for each of the plurality of first influencers is equal to or less than a preset cost. For example, the preset cost may be determined by Equation 2 above. For example, marketing costs for each of the plurality of first influencers may be preset in the server.

In step S405, the server may determine a first influencer whose marketing cost for the influencer is equal to or less than a preset cost among the plurality of first influencers as the second influencer.

In step S406, the server may determine whether the number of the plurality of second influencers is less than or equal to a preset number.

In step S407, if the number of the plurality of second influencers is less than or equal to the preset number, the server may include the plurality of second influencers in the influencer list. In this case, for example, the influencer list may include recommendation scores for each of the plurality of second influencers. For example, the server may determine the degree of association according to a difference between a similarity value matched to a field for each of the plurality of second influencers and a similarity value matched to a field related to the advertisement target. For example, as the difference between the similarity values increases, the degree of association may be determined to be low. For example, the server may determine a high recommendation score for the second influencer having a low degree of association with the field related to the advertisement target.

In step S408, if the number of the plurality of second influencers exceeds the preset number, the server selects the preset number of second influencers from among the plurality of second influencers in descending order of relevance to the field related to the advertisement target. A second influencer may be determined, and the predetermined number of second influencers may be included in the influencer list.

In step S409, the server may transmit information on marketing costs including the list of influencers to the first terminal.

Additionally, for example, when there is no influencer having the same first keyword as at least one of the plurality of second keywords, the server costs marketing expenses for the influencer among the plurality of preset influencers. A plurality of influencers that are less than or equal to this pre-set cost may be determined. And, for example, among the plurality of influencers, a preset number of influencers may be determined in descending order of relevance to the field related to the advertisement target, and the preset number of influencers may be selected as influencers. It can be included in the unser list.

5 shows an example of a cost model according to one embodiment. The embodiments of FIG. 5 can be combined with various embodiments of the present disclosure.

Referring to FIG. 5 , for example, the neural network may include an input layer, one or more hidden layers, and an output layer.

For example, each learning data consisting of a plurality of awareness, information on a plurality of advertisement targets, information on a plurality of advertisement purposes, information on a field related to a plurality of advertisement targets, and correct marketing cost is It may be input to the input layer and output as an output vector after passing through the one or more hidden layers and the output layer. The output vector may be input to a loss function layer connected to the output layer, and the loss function layer may output a loss value using a loss function that compares the output vector with an answer vector for each learning data. Parameters of the neural network may be learned in a direction in which the loss value decreases.

For example, information on recognition, information on advertisement targets, information on advertisement purposes, and information on fields related to advertisement targets may be vectorized through data preprocessing.

For example, recognition may be a value matching the value determined by Equation 1 above. For example, according to the information about gender included in the information about the advertisement target, male, female, and male and female may be vectorized with values of 1, 2, and 3. For example, according to the information about the age group included in the information about the advertisement target, 10 to 70 years of age and all age groups may be vectorized with a value of 1 to 8. For example, sales increase, awareness increase, corporate promotion, product promotion, and event participation may be vectorized as a value of 1 to 5 according to information about advertising purpose. For example, medical, health, beauty, fashion, household goods, interior, children's, household appliances, finance, and others may be vectorized with a value of 1 to 10 according to information about fields related to advertisement targets. For example, the server preprocesses data based on awareness, information on advertising targets, information on advertising purposes, information on fields related to advertising targets, and marketing costs for advertising targets [awareness, gender, age group, advertisement It is possible to create an input vector composed of fields related to purpose and advertisement target.

For example, the server inputs each learning data consisting of a plurality of input vectors and correct marketing costs to the input layer of the neural network, passes through one or more hidden layers and output layers, and outputs an output vector. . For example, the server inputs the output vector to a loss function layer connected to the output layer, and uses a loss function that compares the output vector with the correct answer vector for each learning data through the loss function layer to obtain a loss value. can output For example, the server may generate the cost model by learning parameters of the neural network in a direction in which the loss value decreases.

6 is a block diagram illustrating a configuration of a server according to an exemplary embodiment. One embodiment of FIG. 6 may be combined with various embodiments of the present disclosure.

As shown in FIG. 6 , the server 600 may include a processor 610 , a communication unit 620 and a memory 630 . However, not all components shown in FIG. 6 are essential components of the server 600 . The server 600 may be implemented with more components than those shown in FIG. 6, or the server 600 may be implemented with fewer components than those shown in FIG. For example, the server 600 according to some embodiments may further include a user input interface (not shown), an output unit (not shown), etc. in addition to the processor 610, the communication unit 620, and the memory 630. .

Processor 610, typically controls the overall operation of the server (600). The processor 610 may include one or more processors to control other elements included in the server 600 . For example, the processor 610 may generally control the communication unit 620 and the memory 630 by executing programs stored in the memory 630 . Also, the processor 610 may perform the functions of the server 600 described in FIGS. 3 to 5 by executing programs stored in the memory 630 .

According to an embodiment, the processor 610 may receive information on an advertisement target from the first terminal through the communication unit 620 . For example, the information on the advertisement target includes the name of the advertisement target, information on the advertisement target, information on the purpose of the advertisement, information on a field related to the advertisement target, a website related to the advertisement target, and the advertisement target It may include a SNS channel (social network service) of.

The processor 610 may determine awareness based on values related to the advertisement target for the first period preset through the memory 630 . For example, the values related to the advertisement subject may include the number of times the name of the advertisement subject is searched, the number of visitors to the website related to the advertisement subject, and the value related to the SNS channel of the advertisement subject.

The processor 610 executes the advertisement through a cost model using a neural network based on the awareness, the information about the advertisement target, the information about the purpose of the advertisement, and the information about the field related to the advertisement target through the memory 630. You can determine the marketing cost for the target.

The processor 610 may determine an influencer list based on marketing costs for the advertisement target and information on the advertisement target through the memory 630 .

The processor 610 may transmit information on the marketing cost including the marketing cost for the advertisement target and the influencer list to the first terminal through the communication unit 620 .

The processor 610 may receive an approval message including at least one influencer from among the influencer list from the first terminal through the communication unit 620 .

The processor 610 may determine the recognition by Equation 1 described above. For example, a plurality of advertisement targets may correspond to the same field as the field related to the advertisement target.

The processor 610 may change the marketing cost for the advertisement target based on a difference between the awareness for the first preset period and the awareness for the second preset period. For example, the preset first period may be a period before receiving the approval message from the first terminal. For example, the preset second period may be a period after receiving the approval message from the first terminal.

The processor 610 may acquire a plurality of first keywords for each of a plurality of influencers preset through the memory 630 based on first web crawling. The processor 610 may obtain a plurality of second keywords associated with the name of the advertisement target through second web crawling. The processor 610 may determine a plurality of first influencers having the same first keyword as at least one of the plurality of second keywords from among the plurality of preset influencers. The processor 610 may determine a plurality of second influencers from among the plurality of first influencers. A marketing cost for each of the plurality of second influencers may be equal to or less than a preset cost. The processor 610 may determine the degree of association with the field related to the advertisement target for each of the plurality of second influencers through the memory 630 . The processor 610 may determine a preset number of second influencers from among the plurality of second influencers in descending order of relevance to the field related to the advertisement target. For example, the influencer list may include the preset number of second influencers.

The processor 610 may transmit a measurement message for measuring awareness of the advertisement target to a plurality of preset second terminals through the communication unit 620 .

The processor 610 may receive a response message to the measurement message from at least one second terminal among the plurality of preset second terminals through the communication unit 620 . The processor 610 may adjust the recognition based on the number of response messages.

For example, a neural network may include an input layer, one or more hidden layers, and an output layer.

Through the memory 630, the processor 610 performs each learning consisting of a plurality of awareness, information on a plurality of advertisement targets, information on a plurality of advertisement purposes, information on fields related to a plurality of advertisement targets, and correct marketing costs. Data may be input to the input layer of the neural network, and may be output as an output vector by passing through one or more hidden layers and an output layer. The processor 610 inputs the output vector to the loss function layer connected to the output layer, and calculates a loss value using a loss function that compares the output vector with the correct answer vector for each learning data through the loss function layer. The cost model may be generated by learning parameters of the neural network in a direction in which the loss value decreases.

The communication unit 620 may include one or more components that allow the server 600 to communicate with other devices (not shown) and servers (not shown). The other device (not shown) may be a computing device such as the server 1000 or a sensing device, but is not limited thereto. The communication unit 620 may receive a user input from another electronic device or data stored in an external device from an external device through a network.

For example, the communication unit 620 may transmit/receive a message for establishing a connection with at least one device. The communication unit 620 may transmit information generated by the processor 610 to at least one device connected to the server. The communication unit 620 may receive information from at least one device connected to the server. The communication unit 620 may transmit information related to the received information in response to information received from at least one device.

The memory 630 may store programs for processing and controlling the processor 610 . For example, the memory 630 may store information input to a server or information received from another device through a network. For example, the memory 630 may store information on an advertisement target, an approval message including at least one influencer, and a response message to the measurement message. Also, the memory 630 may store data generated by the processor 610 . For example, the memory 630 stores awareness, marketing costs for advertisement targets, a plurality of first influencers, a plurality of second influencers, relevance, an influencer list, and marketing costs for changed advertisement targets. can be saved The memory 630 may store information input to or output from the server 600 . For example, the memory 630 may include a plurality of preset first categories, a plurality of preset second categories, a preset period, a preset first period, a preset second period, a preset number, a default value, and a preset number. It may include a plurality of second terminals, a preset ratio, a plurality of preset influencers, and a preset number.

The memory 630 may be a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (eg SD or XD memory, etc.), RAM (RAM, Random Access Memory) SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk , an optical disk, and at least one type of storage medium.

The embodiments described above may be implemented as hardware components, software components, and/or a combination of hardware components and software components. For example, the devices, methods and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate (FPGA). array), programmable logic units (PLUs), microprocessors, or any other device capable of executing and responding to instructions. A processing device may run an operating system (OS) and one or more software applications running on the operating system. A processing device may also access, store, manipulate, process, and generate data in response to execution of software. For convenience of understanding, there are cases in which one processing device is used, but those skilled in the art will understand that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it can include. For example, a processing device may include a plurality of processors or a processor and a controller. Other processing configurations are also possible, such as parallel processors.

Software may include a computer program, code, instructions, or a combination of one or more of the foregoing, which configures a processing device to operate as desired or processes independently or collectively. The device can be commanded. Software and/or data may be any tangible machine, component, physical device, virtual equipment, computer storage medium or device, intended to be interpreted by or provide instructions or data to a processing device. , or may be permanently or temporarily embodied in a transmitted signal wave. Software may be distributed on networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer readable media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer readable medium. The computer readable medium may include program instructions, data files, data structures, etc. alone or in combination. Program commands recorded on the medium may be specially designed and configured for the embodiment or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. - includes hardware devices specially configured to store and execute program instructions, such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of program instructions include high-level language codes that can be executed by a computer using an interpreter, as well as machine language codes such as those produced by a compiler. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

As described above, although the embodiments have been described with limited drawings, those skilled in the art can apply various technical modifications and variations based on the above. For example, the described techniques may be performed in an order different from the method described, and/or components of the described system, structure, device, circuit, etc. may be combined or combined in a different form than the method described, or other components may be used. Or even if it is replaced or substituted by equivalents, appropriate results can be achieved.

Therefore, other implementations, other embodiments, and equivalents of the claims are within the scope of the following claims.

Claims (5)

A method in which a server provides information on marketing costs to a first terminal using a neural network, the method comprising:
receiving information on an advertisement target from the first terminal;
The information on the advertisement target includes the name of the advertisement target, information on the advertisement target, information on the purpose of advertising, information on a field related to the advertisement target, a website related to the advertisement target, and an SNS channel of the advertisement target. (social network service) included,
determining awareness based on values related to the advertisement target for a preset first period;
The values related to the advertisement target include the number of times the name of the advertisement target is searched, the number of visitors to the website related to the advertisement target, and a value related to the SNS channel of the advertisement target,
A value matched to the awareness, a value matched to the gender of the advertisement target, the value matched to the gender of the advertisement target, and generating an input vector composed of values matched to the age group of the advertisement target, values matched to the advertisement purpose, and values matched to fields related to the advertisement target;
determining a marketing cost for the advertisement target through the cost model based on the input vector being input to a cost model using a neural network;
determining an influencer list based on marketing costs for the advertisement target and information on the advertisement target;
transmitting marketing cost information for the advertisement target and marketing cost information including the list of influencers to the first terminal; and
Receiving an approval message including at least one influencer from the influencer list from the first terminal,
A marketing cost for the advertisement target is changed based on a difference between the awareness for the first preset period and the awareness for a preset second period;
The preset first period is a period before receiving the approval message from the first terminal,
The preset second period is a period after receiving the approval message from the first terminal,
The marketing cost for the advertisement target is changed by the following equation,

In the above equation, m _c is marketing cost for the changed advertisement target, a is awareness for the first preset period, a _m is awareness for the second preset period, and a _d is a default value for awareness, m _t is a marketing cost for the advertisement target,
A plurality of first keywords for each of a plurality of preset influencers are obtained through a first web crawling;
A plurality of second keywords associated with the name of the advertisement target are obtained through second web crawling;
A plurality of first influencers having a first keyword identical to at least one of the plurality of second keywords among the plurality of preset influencers are determined;
A plurality of second influencers are determined from among the plurality of first influencers,
The marketing cost for each of the plurality of second influencers is less than or equal to a preset cost,
For each of the plurality of second influencers, a degree of association with a field related to the advertisement target is determined;
Among the plurality of second influencers, a predetermined number of second influencers are determined in order of low relevance to the field related to the advertisement target,
The influencer list includes the second influencer of the preset number,
Way.
According to claim 1,
The recognition is determined by the following equation,

In the above equation, a is the recognition, s is the number of times the name of the advertisement target is searched, s _d is a default value for the number of times the name of the advertisement target is searched, and s ₀ is the name of a plurality of advertisement targets. the average number of times, v is the number of visitors to the website related to the advertisement target, v _d is a default value for the number of visitors, and v ₀ is the average number of visitors to the website related to the plurality of advertisement targets , wherein i is the number of hits to the SNS channel of the advertisement target, i _d is a default value for the number of hits, i ₀ is the average number of hits to the SNS channels of the plurality of advertisement targets, and c is the number of hits to the advertisement target The number of times the SNS channel of the target is added, c _d is a default value for the number of times added, c ₀ is the average number of times the SNS channels of the plurality of advertisement targets are added,
The plurality of advertisement targets correspond to the same field as the field related to the advertisement target,
Way.
delete According to claim 1,
Transmitting a measurement message for measuring awareness of the advertisement target to a plurality of preset second terminals; and
Receiving a response message to the measurement message from at least one second terminal among the plurality of second terminals set in advance,
The recognition is adjusted based on the number of the reply messages,
Way.
According to claim 1,
The neural network includes an input layer, one or more hidden layers and an output layer;
Each learning data consisting of a plurality of awareness, information on a plurality of advertisement targets, information on a plurality of advertisement purposes, information on a field related to a plurality of advertisement targets, and correct marketing cost is input to the input layer of the neural network. is output as an output vector after passing through the one or more hidden layers and the output layer, the output vector is input to a loss function layer connected to the output layer, and the loss function layer is the correct answer for the output vector and each training data. A loss value is output using a loss function that compares vectors, and parameters of the neural network are learned in a direction in which the loss value becomes smaller, and the cost model is generated.
Way.

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