KR102562282B1 - Propensity-based matching method and apparatus - Google Patents

Abstract

Embodiments provide a propensity-based matching method and apparatus. According to an embodiment, a propensity-based matching method may include determining, by a processor, a first candidate group including a plurality of candidates by performing primary matching filtering based on a user's interests; determining, by the processor, a second user personality type of the user through a second personality type test; determining, by the processor, a second candidate personality type whose degree of fitness for the second user personality type is greater than or equal to a first threshold value based on the compatibility chart of the second personality type test; determining, by the processor, a second candidate group including a plurality of candidates by performing secondary matching filtering on the first candidate group; determining, by the processor, a first user personality type of the user through a first personality type test; determining, by the processor, a first candidate personality type whose degree of fitness for the second user personality is equal to or greater than a second threshold based on the compatibility chart of the first personality type test; and outputting, by the processor, a candidate corresponding to the first candidate personality type among a plurality of candidates belonging to the second candidate group as a matching result.

Description

Propensity-based matching method and apparatus {PROPENSITY-BASED MATCHING METHOD AND APPARATUS}

Embodiments of the present invention relate to a technique for matching people based on interests, and to a technique for matching people by fusing different personality type tests.

MBTI (Myers-Briggs Type Indicator) finds individual preferences when recognizing and judging through self-report questions that individuals can easily respond to, and identifies how these preference tendencies affect human behavior in real life It is a psychological test designed to be applied to

Through the MBTI type, you can find your type among 16 types, and it is relative. It is based on Carl Jung's theory of personality types. This test specifies four categories: introversion or extroversion, sensing or intuition, thinking or feeling, judging, and perceiving.

Publication No. 10-2022-0015162

According to an embodiment, a propensity-based matching system that provides a more accurate matching service between users by integrating various types of tests for estimating individual propensity is provided.

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 propensity-based matching method according to an embodiment may include determining, by a processor, a first candidate group including a plurality of candidates by performing primary matching filtering based on a user's interests; determining, by the processor, a first user personality type of the user through a first personality type test; determining, by the processor, a first candidate personality type whose degree of fitness for the second user personality is equal to or greater than a second threshold based on the compatibility chart of the first personality type test; determining, by the processor, a second user personality type of the user through a second personality type test; determining, by the processor, a second candidate personality type whose degree of fitness for the second user personality type is greater than or equal to a first threshold value based on the compatibility chart of the second personality type test; determining, by the processor, a second candidate group including a plurality of candidates by performing secondary matching filtering on the first candidate group; and outputting, by the processor, a candidate corresponding to the first candidate personality type among a plurality of candidates belonging to the second candidate group as a matching result.

The determining of the first candidate group may include determining an interest of the user by using an interest determination tree based on a user input; and determining the first candidate group including a plurality of candidates having the same interest as the user.

The determining of the second candidate group may include determining the second candidate group including a plurality of candidates corresponding to the second candidate personality type from among the first candidate group.

The determining of the first user personality type may include determining a candidate personality of the user by performing a disposition determination test of the first personality type test; and determining a first user personality type of the user by performing a personality determination test of the first personality type test corresponding to the candidate personality.

The determining of the candidate personality of the user may include displaying one or more question sentences; receiving the user's response sentence corresponding to each of the one or more question sentences; calculating a matching rate of words included in the user's response sentence matched to a word set for each personality type of the first personality type test; and determining a candidate personality corresponding to a word set having the matching rate equal to or greater than a third threshold.

The operation of determining the first user personality type of the user by performing the personality determination test may include displaying each of the expression sentences included in the sentence set corresponding to the candidate personality at regular time intervals, and selecting each expression sentence. receiving input; summing scores of expression sentences selected by the selection input; and determining the candidate personality as the first user personality type of the user when the result of the sum is greater than or equal to a fourth threshold.

The operation of determining the first user personality type of the user by performing the personality determination test may include displaying each of the expression sentences included in the sentence set corresponding to the candidate personality at regular time intervals, and selecting each expression sentence. receiving input; and determining the first user personality type by inputting the selected one or more expression sentences into a personality type examination model composed of a neural network.

The personality type test model includes an input layer, one or more hidden layers, and an output layer, and the input layer generates an identifier corresponding to the input expression sentence by using a matching table storing a matching relationship between expression sentences and identifiers. Each learning data composed of the learning expression sentence and the first user personality type of the correct answer is input to the input layer of the personality type examination model model, passes through the one or more hidden layers and the output layer, and is output. A vector is output, the output vector is input to a loss function layer connected to the output layer, and the loss function layer outputs a loss value using a loss function that compares the output vector with the correct answer vector for each learning data. Parameters of the personality type examination model may be learned in a direction in which the loss value becomes smaller.

A computer program according to an embodiment may be combined with hardware and stored in a computer readable recording medium to execute the method.

A computer readable recording medium according to an embodiment may store a computer program that is combined with hardware to execute the method.

According to an embodiment, the propensity-based matching system may provide a more accurate matching service between users by integrating various types of tests for estimating individual propensities.

The test may include a test for estimating a user's propensity based on interests. The propensity-based matching system can improve users' satisfaction with matching results by estimating the user's propensity based on interests.

The test may include a test for estimating a generally used user's propensity. The propensity-based matching system can further improve the reliability of matching results by incorporating widely used and highly reliable tests.

The propensity-based matching system roughly selects a suitable personality type through a personality type test that is generally widely used, and selects a more suitable personality type through a personality type test that is precisely classified based on interests for the selection result. can increase the accuracy of

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 is a diagram showing the overall configuration of a propensity-based matching system according to an embodiment.
4 is a diagram illustrating an overall operation of a propensity-based matching method according to an embodiment.
5 is an example of an interest decision tree for performing primary matching filtering based on user interests according to an embodiment.
6 is a diagram illustrating an example of a disposition determination test of a first personality type test according to an embodiment.
7 is a diagram illustrating an example of a personality determination test of a first personality type test according to an embodiment.
8 is a diagram illustrating an example of a compatibility chart of a second personality type test according to an embodiment.
9 illustrates an exemplary graph in which time of a selection input of a personality determination test of a first personality type test is clustered according to an embodiment.
10 is a diagram illustrating a configuration of a server of a propensity-based matching system according to an 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 WAP 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, Visual Basic, or Visual C 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 uses a server program provided in various ways according to operating systems such as DOS, Windows, Linux, UNIX, and Macintosh in general server hardware. As a representative example, a website used in a Windows environment, Internet Information Server (IIS), and CERN, NCSA, APPACH, etc. used in a Unix environment can be used. 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 is based on the TCP/IP protocol and several services that exist on its upper layer, such as HTTP (HyperText Transfer Protocol), Telnet, FTP (File Transfer Protocol), DNS (Domain Name System), SMTP (Simple Mail Transfer Protocol), SNMP ( Simple Network Management Protocol), Network File Service (NFS), and Network Information Service (NIS).

The 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 is a diagram showing the overall configuration of a propensity-based matching system according to an embodiment.

According to an embodiment, the propensity-based matching system may provide a more accurate matching service between users by integrating various types of tests for estimating individual propensities.

The test may include a test for estimating a user's propensity based on interests. The propensity-based matching system can improve users' satisfaction with matching results by estimating the user's propensity based on interests.

The test may include a test for estimating a generally used user's propensity. The propensity-based matching system can further improve the reliability of matching results by incorporating widely used and highly reliable tests.

The propensity-based matching system roughly selects a suitable personality type through a personality type test that is generally widely used, and selects a more suitable personality type through a personality type test that is precisely classified based on interests for the selection result. can increase the accuracy of

To this end, the propensity-based matching system may include a server 300, a network 330, a terminal 320, and a database 310.

The server 300 may first perform matching filtering based on interest and determine a filtered candidate group. The server 300 may first estimate a user's propensity by using a propensity test conducted based on interests. The server 300 may select a candidate's propensity corresponding to the estimated primary propensity of the user by using a compatibility chart for the propensity test conducted based on interests. The server 300 may secondarily estimate the user's propensity using a general propensity test. The server 300 may determine a filtered candidate group by performing secondary matching filtering based on the estimated secondary propensity of the user using the compatibility chart for the general propensity test. The server 300 may select a candidate corresponding to the propensity of the candidate corresponding to the primary propensity from the secondary match-filtered candidate group. The server 300 may output the selected candidate as a final matching result.

The propensity test based on interests can be largely carried out in two steps. The server 300 may first request the user to express a response to the problem in a sentence. The server 300 may determine candidate characteristics based on the number of words matching a preset word set with respect to the user's response sentence. Next, the server 300 may display preset sentences corresponding to candidate personalities one by one at regular time intervals, and may request selection of whether or not they correspond to the user himself/herself. The server 300 may calculate a final score by integrating the scores of the selected sentences. The server 300 may estimate the user's propensity with the corresponding personality when the final score is equal to or greater than the threshold value.

Definitions of terms are described below.

A concern decision tree refers to a data structure representing a hierarchical relationship between an upper item and a lower item of interest. The server 300 may classify people having the same interest as the interest of the last depth of the interest determination tree into the same group.

The compatibility chart of the second personality type test refers to a chart representing compatibility between the same or different first personality types. The MBTI Compatibility Chart refers to a chart representing the degree of compatibility between MBTI personality types. The compatibility chart of the first personality type test refers to a chart representing compatibility between the second personality types.

A user means a subject that performs a propensity-based matching service. A candidate means a potential target that can be matched with a user.

The second personality type test refers to a personality type test that is generally widely used. For example, the second personality type test may include the MBTI test. The first personality type test refers to a personality type test for classifying personality types based on interests. The first personality type test is a personality type test self-defined based on a plurality of data. For example, the first personality type test may include an ABCD personality type test.

The ABCD personality type test refers to a test that determines a personality type through judgment of disposition and personality based on statistics of personality types of people who are interested in a specific interest.

The second user personality type refers to the personality type of the user determined through the second personality type test. The second candidate personality type refers to the personality type of the candidate determined through the second personality type test.

The first candidate group refers to a group including a plurality of candidates selected through primary matching filtering. The second candidate group refers to a group including a plurality of candidates selected through secondary matching filtering on the first candidate group.

Here, matching filtering refers to a process of filtering candidates that are determined to be suitable for matching with a user from among a plurality of candidates. Primary matched filtering refers to matched filtering based on a user's interests. The secondary matching filtering refers to matching filtering based on the compatibility chart of the second personality type test.

The question sentence refers to a sentence used in the disposition judgment test of the first personality type test. The response sentence refers to a sentence input by the user in response to the question sentence. The expression sentence refers to a sentence used in the personality judgment test of the first personality type test.

According to one embodiment, the server 300 may include a processor, a memory, and an input/output unit.

The processor may determine a first candidate group including a plurality of candidates by performing primary matched filtering based on the interest of the user. The processor may determine the interests of the user using the interest decision tree based on the user input. The processor may determine a first candidate group including a plurality of candidates having the same interest as the user.

The processor may determine the first user personality type of the user through the first personality type test. The processor may determine the candidate personality of the user by performing the disposition determination test of the first personality type test. The processor may determine the first user personality type of the user by performing a personality determination test of the first personality type test corresponding to the candidate personality.

The propensity judgment test may be performed in the following process. The processor may present one or more query sentences. The processor may receive a user's response sentence corresponding to each of the one or more question sentences. The processor may calculate a matching rate of words included in the user's response sentence that match the word set for each personality type of the first personality type test. The processor may determine a candidate personality corresponding to a word set having a matching rate equal to or greater than the third threshold.

The personality determination test may be selectively performed through two embodiments.

According to an embodiment, the processor may perform the personality determination test through a score summing method. The processor may display each of the expression sentences included in the sentence set corresponding to the candidate personality at regular time intervals, and receive a selection input for each expression sentence. The processor may sum the scores of expression sentences selected by the selection input. The processor may determine the candidate personality as the first user personality type of the user when the summation result is equal to or greater than the fourth threshold value.

According to another embodiment, the processor may perform a personality determination test using a neural network. The processor may display each of the expression sentences included in the sentence set corresponding to the candidate personality at regular time intervals, and receive a selection input for each expression sentence.

The processor may determine the first user personality type by inputting the selected one or more expression sentences to a personality type examination model configured as a neural network.

Here, the personality type examination model includes an input layer, one or more hidden layers, and an output layer, and the input layer outputs an identifier corresponding to an input expression sentence using a matching table storing a matching relationship between expression sentences and identifiers. You can pass it to the next layer.

Each training data composed of the learning expression sentence and the correct answer first user personality type is input to the input layer of the personality type examination model model, passes through one or more hidden layers and an output layer, and outputs an output vector, and the output vector is output to the output layer. It is input to the connected loss function layer, and the loss function layer outputs a loss value using a loss function that compares the output vector with the correct answer vector for each learning data. direction can be learned.

[Equation 1]

The loss function follows Equation 1 above. In Equation 1, n is the number of training data for each class, y and j are identifiers representing classes, C is a constant value, M is the number of classes, x_y is a probability value that training data belongs to class y, x_j is training data is a probability value belonging to class j, and L is a loss value.

Since Equation 1 reflects the number of learning data per class, a class with a small number of training data may have a small effect on learning, and a class with a large number of training data may have a large effect on learning.

The sentence selection time is defined as the time between the time when the expression sentence is displayed and the time when the selection input is received. In general, it is understood that the shorter the sentence selection time is, the shorter the time for the user to think is, and it can be assumed that the expression sentence is recognized as a sentence that clearly represents the user. It is understood that the longer the sentence selection time is, the longer the user spends time thinking about, and it can be estimated that he is not sure whether the expression sentence is a sentence representing himself.

The more samples that are expressive sentences recognized as clearly representing the user, the higher the accuracy of the user's personality judgment. On the other hand, if the sample is small, the accuracy of the user's personality judgment may be low.

According to an embodiment, the propensity-based matching system can improve the accuracy of character determination of the current user by using data of existing users having a large sample when the current user sample is small.

For example, a group with a large sample includes a group of multiple users in which the number of sentences selected within a certain time is greater than or equal to a threshold value, for example, 500 sentences, 50% of which are 50% of 1000 selected sentences, are selected within a certain time. can do. For example, a group with a small sample may include a group of users in which the number of sentences selected within a certain time period is less than a threshold value.

The propensity-based matching system can assign a higher weight to the selected sentence as the sentence selection time from the sample is shorter. The propensity-based matching system may classify sentence selection times by grouping them.

The server 300 may cluster one or more selected sentences into a plurality of groups according to a plurality of criteria. A group of selected sentences may be determined as a plurality of groups through a clustering technique using a neural network based on information about the selected sentence and information on the first user personality type. The selected sentence information refers to information about characteristics of the selected sentence itself, such as the type of the selected sentence, the length of the selected sentence, and the sentence selection time of the selected sentence. The information on the first user personality type includes the finally determined first user personality type and a difference between the candidate personality indicated by the selection sentence and the information on the first user personality type. The server 300 may map a plurality of pieces of data about the selected sentence information and the first user personality type information to spatial coordinates. The server 300 may determine one or more density areas for a plurality of data. The server 300 may group selected sentences corresponding to cores and boundaries of one or more density areas. For example, the server 300 may group selected sentences into a plurality of groups including four groups through the above process. The server 300 may calculate an average value of sentence selection times in each of the four grouped groups. The server 300 may set 4 time boundaries and 5 time grades by arranging the calculated average values.

The server 300 may select a plurality of users whose number of valid sentences matching valid sentences of a group with a small sample is greater than or equal to a%. For example, if the number of valid sentences in a group with a small sample among 1000 is 100, the server 300 may select a plurality of users in a group with a large sample having 87 or more matching valid sentences, 87% or more of them. there is.

If there is a difference of 100-a% between the number of valid sentences selected by users with a small sample and the number of valid sentences selected by users with a large sample, the smaller the difference, the more users with a larger sample select the remaining valid sentences. It can be justified to infer to a group with a small sample.

The server 300 may calculate personality contribution scores for each user including a large sample of people and a small sample of people. Personality contribution scores reflect the tendencies overtly exhibited by a small sample of people, with the intention of using a larger explicit sample of people with similar traits.

For example, the server 300 calculates the personality contribution score of each user through Equation 2, and whether to infer a valid selection sentence for a group with a large sample to a group with a small sample based on the personality contribution score of each user. can decide whether

The weight reflected in the personality contribution score may be determined according to a grouping grade based on whether the sentence selection time is shorter. For example, the sentence selection time groups may be classified into 5 groups, and when the rating i of each group is = 1, 2, 3, 4, or 5, a weight of 1, 2, 4, 8, or 16 may be applied.

[Equation 2]

In Equation 2, the personality contribution score for the first group, which is a small sample group, is can be defined as Here, m is the number of sample data (valid selection sentences) of each class of sentence selection time, j is an identification number of sample data, and xi,j is a score uniquely assigned to each selection sentence. n is the number of classes, for example, n = 5 (i is a class identification number, 5 = A, 4 = B ?? 1 = E), is a set of sample data of the i-th rank, denotes a value obtained by standardizing the value of each sample data with the maximum value of the i-th grade sample data set, and cannot exceed a maximum of 1. represents the personality contribution score for each grade as the average value of the standardized values of the sample data. In , n differs by 1 for each grade, cannot exceed a maximum of 1, so the higher the personality contribution score of the higher grade, the larger the value is. in other words, denotes the weight for the class.

In Equation 2, with respect to the second group, which is a group with a large number of samples, for example, the sentence selection time group can be classified into 5 groups, and each group's rating i = 1, 2, 3, 4, 5 A weight of 1, 2, 4, 8, or 16 may be applied.

The personality contribution score for group 2 was can be defined as q is the number of sample data (valid selection sentences) of each class of sentence selection time, l is an identification number of sample data, xk,l is a score uniquely assigned to each selection sentence. p is the number of classes, p = 5 (k is a class identification number, 5 = A, 4 = B ?? 1 = E). is a set of sample data of the kth rank, is a value obtained by standardizing the value of each sample data with the maximum value of the set of sample data of the kth rank, and cannot exceed a maximum of 1. represents the personality contribution score for each grade as the average value of the standardized values of the sample data. In , p differs by 1 for each grade, cannot exceed a maximum of 1, so the higher the personality contribution score of the higher grade, the larger the value is. in other words, denotes the weight for the class.

In Equation 2, Means the absolute value of the difference between the personality contribution score for the first group and the personality contribution score for the second group. The functions of y and z are:

Depending on the value of y, z will be distributed between 0.5 and 1.5. The server 300 may calculate z for personality contribution scores between users with a small sample and users with a large sample. The server 300 may determine a population distribution according to its own personality type based on each valid selection sentence selected by a plurality of people having z of a constant value, eg, 1.0 or less.

The input layer of the neural network may receive a user's selected sentence. The last layer may be a layer having a plurality of nodes representing each ABCD type. The last layer outputs a probability value for each ABCD type, and the weight of the connection line of the node representing each type can be adjusted according to the determined population distribution.

For example, if there are 200 people in A1B2C1D1, 100 people in A1B2C1D2, and 300 people in A1B1C1D1, where z is 1.0 or less, the value multiplied by the weight of the connection line of A1B2C1D1 is 1 + (200/(200+100+300))*p Or it can be a normalized value of this value. Here, p may be a parameter that adjusts the influence of population distribution.

The processor may determine a first candidate personality type having a degree of conformity to a second user personality equal to or greater than a second threshold value based on the compatibility chart of the first personality type test.

Externally, the second user personality type of the user may be determined through the second personality type test. The server 300 may receive the second user personality type. The processor may determine a second candidate personality type whose degree of conformity to the second user personality type is equal to or greater than the first threshold value based on the compatibility chart of the second personality type test.

The processor may determine a second candidate group including a plurality of candidates by performing secondary matching filtering on the first candidate group. The processor may perform secondary matching filtering by determining a second candidate group including a plurality of candidates corresponding to the second candidate personality type from among the first candidate group.

The processor may output a candidate corresponding to the first candidate personality type among the plurality of candidates belonging to the second candidate group as a matching result.

4 is a diagram illustrating an overall operation of a propensity-based matching method according to an embodiment.

According to an embodiment, in operation 401 , the server 300 may determine a first candidate group including a plurality of candidates by performing primary matching filtering based on the interest of the user. The server 300 may determine the interest of the user by using the interest determination tree based on the user input. The server 300 may determine a first candidate group including a plurality of candidates having the same interest as the user.

According to an embodiment, in operation 403, the server 300 may determine a first user personality type of the user through a first personality type check. The server 300 may determine the candidate personality of the user by performing a disposition determination test of the first personality type test. The server 300 may determine the first user personality type of the user by performing a personality determination test of the first personality type test in response to the candidate personality.

According to an embodiment, in operation 405, the server 300 may determine a first candidate personality type whose degree of fitness for the second user personality is equal to or greater than a second threshold based on the compatibility chart of the first personality type test. .

According to an embodiment, in operation 407, the server 300 may receive the second user personality type of the user determined through the externally performed second personality type test.

According to an embodiment, in operation 409, the server 300 may determine a second candidate personality type whose degree of fitness for the second user personality type is equal to or greater than a first threshold based on the compatibility chart of the second personality type test. there is.

According to an embodiment, in operation 411, the server 300 may determine a second candidate group including a plurality of candidates by performing secondary matching filtering on the first candidate group. The server 300 may perform secondary matching filtering by determining a second candidate group including a plurality of candidates corresponding to the second candidate personality type from among the first candidate group.

According to an embodiment, in operation 413, the server 300 may output a candidate corresponding to the first candidate personality type among a plurality of candidates belonging to the second candidate group as a matching result.

5 is an example of an interest decision tree for performing primary matching filtering based on user interests according to an embodiment.

The server 300 may first perform matching filtering based on interest and determine a filtered candidate group. The processor may determine the interests of the user using the interest decision tree based on the user input. The processor may determine a first candidate group including a plurality of candidates having the same interest as the user.

Matching filtering refers to a process of filtering candidates determined to be suitable for matching with a user from among a plurality of candidates. Primary matched filtering refers to matched filtering based on a user's interests. A concern decision tree refers to a data structure representing a hierarchical relationship between an upper item and a lower item of interest. The server 300 may classify people having the same interest as the interest of the last depth of the interest determination tree into the same group.

Referring to FIG. 5 , an example of a concern decision tree is shown. The interest decision tree may be composed of layers of 4 depths. From the first layer to the fourth layer, concerns can be gradually specified.

For example, the server 300 may display a sentence such as 'Please select an interest' through the terminal 320 . The terminal 320 may display keywords representing various interests such as human relationships, careers, finances, or hobbies.

For example, if the user selects 'career' from keywords, the terminal 320 may display an additional question such as 'what are you interested in in 'career'.' The terminal 320 may display keywords representing various interests such as employment, entrepreneurship, job change, or career change.

For example, the fourth layer may be changed by user definition. Users can define service, platform, game, AI, or user-added keywords in response to IT keywords.

6 is a diagram illustrating an example of a disposition determination test of a first personality type test according to an embodiment.

The server 300 may secondarily estimate the user's propensity using a propensity test conducted based on interests. The processor may determine the first user personality type of the user through the first personality type test. The processor may determine the candidate personality of the user by performing the disposition determination test of the first personality type test.

The first personality type test refers to a personality type test for classifying personality types based on interests. The first personality type test is a personality type test self-defined based on a plurality of data. For example, the first personality type test may include an ABCD personality type test.

The question sentence refers to a sentence used in the disposition judgment test of the first personality type test. The response sentence refers to a sentence input by the user in response to the question sentence.

Referring to FIG. 6 , a plurality of question sentence sets may be set in advance to correspond to interests. The server 300 may display a question sentence such as 'If today was the last day of your life, what would you like to do?' through the terminal 320 . The user himself may input a response sentence such as 'I want to gather all my friends and have a drink' into the terminal 320 .

The processor may calculate a matching rate of words included in the user's response sentence that match the word set for each personality type of the first personality type test stored in the database 310 . The processor may determine a candidate personality corresponding to a word set having a matching rate equal to or greater than the third threshold.

7 is a diagram illustrating an example of a personality determination test of a first personality type test according to an embodiment.

The server 300 may secondarily estimate the user's propensity using a propensity test conducted based on interests. The processor may determine the first user personality type of the user through the first personality type test. The processor may determine the first user personality type of the user by performing a personality determination test of the first personality type test corresponding to the candidate personality. The expression sentence refers to a sentence used in the personality judgment test of the first personality type test.

Referring to FIG. 7 , various expression sentences corresponding to interests may be displayed through the terminal 320 at regular time intervals. For example, expression sentences such as 'I am deeply moved by your kindness' and 'I clearly remember an important day or event' may be displayed through the terminal 320 at regular time intervals.

When the displayed expression sentence corresponds to the user, the user may select the corresponding sentence. For example, in FIG. 7 , 'I am deeply moved by kindness' is a selected state, but 'I clearly remember an important day or event' may be an unselected state.

The server 300 may sum the weights of the standardized evaluation items corresponding to each selected sentence, and determine one or more personalities among a plurality of personality categories according to the summation result.

8 is a diagram illustrating an example of a compatibility chart of a second personality type test according to an embodiment.

The server 300 may determine a filtered candidate group by performing secondary matching filtering based on the estimated secondary propensity of the user using the compatibility chart for the general propensity test.

The compatibility chart of the second personality type test refers to a chart representing compatibility between the same or different first personality types. The MBTI Compatibility Chart refers to a chart representing the degree of compatibility between MBTI personality types.

Referring to FIG. 8, an MBTI Compatibility Chart is shown. The horizontal and vertical axes display all personality types defined by the MBTI, and the compatibility between the two personality types, that is, the probability that the personalities match each other, is displayed in the intersection area of any two personality types.

9 illustrates an exemplary graph in which time of a selection input of a personality determination test of a first personality type test is clustered according to an embodiment.

Selected sentences related to the user's target brand may be determined as a plurality of groups through a clustering technique using a neural network based on the selected sentence information and the first user personality type information. Here, the selected sentence information refers to information about characteristics of the selected sentence itself, such as the type of the selected sentence, the length of the selected sentence, and the sentence selection time of the selected sentence. The information on the first user personality type includes the finally determined first user personality type and a difference between the candidate personality indicated by the selection sentence and the information on the first user personality type. Clustering may refer to unsupervised learning that groups data having similar properties into a certain number of clusters. Groups may be determined as n types through a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) technique. DBSCAN assumes that if a specific point belongs to a cluster, it must be located close to many other points in the cluster, and radius and minimum points can be used for this calculation. Additionally, the element is a value obtained by digitizing the selected sentence information and the first user personality type information and converting them into two-dimensional coordinates. The 2D coordinates may be [selected sentence information values, information values of the first user personality type]. For example, the diameter may be a radius based on a specific data element, and may be referred to as a dense area. The minimum element may indicate how many elements are required around the core point to specify a core point. In addition, each element of the data set can be divided into a core, a border, and an outlier point. Through the neural network, the processor can check the size of the diameter of each element and search for how many elements there are. Thereafter, if m or more elements exist within the diameter range, the processor may determine the corresponding element as a core element. Also, the processor may determine an element included within a range of a diameter from the core element as a boundary element. Further, the processor may determine elements that are not included within a diameter range from the core elements as outlier elements, and the outlier elements may be excluded from the corresponding cluster. Also, if the distance between the core elements is smaller than the diameter, the processor may classify the corresponding elements into the same cluster. For this reason, the time grades are not determined as fixed types, but are flexibly changed according to data collected by the current processor to determine time grades classified as sections with higher accuracy.

Referring to FIG. 9 , the 2D coordinates may be [selected sentence information values, information values of the first user personality type]. The selected sentence information value may be distributed between 0.1, 0.2, 0.3, 0.4 and 0.5, and the information value of the first user personality type may be distributed between 1 and 7. On the graph of FIG. 9 , [selected sentence information values, information values of the first user personality type] may be clustered into a total of four groups.

10 is a diagram illustrating a configuration of a server of a propensity-based matching system according to an embodiment.

According to one embodiment, the server 1000 (eg, the server 300 of FIG. 3 ) may include a memory 1010 , a processor 1020 and an input/output unit 1030 .

According to an embodiment, the processor 1020 may determine a first candidate group including a plurality of candidates by performing primary matched filtering based on the interest of the user. The processor 1020 may determine the interest of the user using the interest decision tree based on the user input. The processor 1020 may determine a first candidate group including a plurality of candidates having the same interest as the user.

According to an embodiment, the processor 1020 may determine the first user personality type of the user through the first personality type test. The processor 1020 may determine a candidate personality of the user by performing a disposition determination test of the first personality type test. The processor 1020 may determine the first user personality type of the user by performing a personality determination test of the first personality type test in response to the candidate personality.

According to an embodiment, the processor 1020 may determine a first candidate personality type whose degree of fitness for the first user personality type is equal to or greater than a second threshold value based on the compatibility chart of the first personality type test.

According to an embodiment, the processor 1020 may determine the second user personality type of the user through the second personality type test.

According to an embodiment, the processor 1020 may determine a second candidate personality type whose degree of fitness for the second user personality type is equal to or greater than a first threshold value based on the compatibility chart of the second personality type test.

According to an embodiment, the processor 1020 may determine a second candidate group including a plurality of candidates by performing secondary matching filtering on the first candidate group. The processor 1020 may perform secondary matching filtering by determining a second candidate group including a plurality of candidates corresponding to the second candidate personality type from among the first candidate group.

According to an embodiment, the processor 1020 may output a candidate corresponding to the first candidate personality type among a plurality of candidates belonging to the second candidate group as a matching result.

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 (10)

determining, by a processor, a first candidate group including a plurality of candidates by performing primary matched filtering based on the interest of the user;
determining, by the processor, a first user personality type of the user through a first personality type test;
determining, by the processor, a first candidate personality type whose degree of fitness for the first user personality type is greater than or equal to a second threshold value based on the compatibility chart of the first personality type test;
receiving, by an input/output unit, a second user personality type of the user determined through a second personality type test;
determining, by the processor, a second candidate personality type whose degree of fitness for the second user personality type is greater than or equal to a first threshold value based on the compatibility chart of the second personality type test;
determining, by the processor, a second candidate group including a plurality of candidates by performing secondary matching filtering on the first candidate group; and
outputting, by the processor, a candidate corresponding to the first candidate personality type among a plurality of candidates belonging to the second candidate group as a matching result;
The secondary matching filtering is performed based on the compatibility chart of the second personality type test and is a process of filtering out candidates determined to be suitable for matching with a user from among a plurality of candidates;
The operation of determining the first user personality type,
determining a candidate personality of the user by performing a disposition determination test of the first personality type test; and
determining a first user personality type of the user by performing a personality determination test of the first personality type test corresponding to the candidate personality;
The operation of determining the first user personality type of the user by performing the personality determination test,
displaying each of the expression sentences included in the sentence set corresponding to the candidate personality at regular time intervals, and receiving a selection input for each expression sentence;
When the user belongs to the first group in which the ratio of valid sentences in which the time of the selection input is less than the threshold time is 50% or less, the personality contribution score for the first group and the valid sentences based on Equation 2 comparing personality contribution scores for a second group in which the ratio exceeds 50%;
determining the first user personality type using one or more valid sentences selected from the second group when the comparison result satisfies a predetermined condition;
[Equation 2]

In Equation 2, the personality contribution score for the first group is where m is the number of sample data (valid selection sentences) of each class of sentence selection time, j is an identification number of sample data, xi,j is a score uniquely assigned to each selection sentence, n is the number of classes, is a set of sample data of the i-th rank,
The personality contribution score for group 2 was where q is the number of sample data (valid selection sentences) of each class in the sentence selection time, l is an identification number of sample data, xk,l a score uniquely assigned to each selection sentence, p is the number of classes, is a set of sample data of the kth rank,
The predetermined condition is a condition in which z is less than 1.0,
, a propensity-based matching method. According to claim 1,
The operation of determining the first candidate group,
determining an interest of the user using an interest decision tree based on a user input; and
Determining the first candidate group including a plurality of candidates having the same interest as the user
Including, propensity-based matching method. According to claim 1,
The operation of determining the second candidate group,
Determining a second candidate group including a plurality of candidates corresponding to the second candidate personality type from among the first candidate group
Including, propensity-based matching method. delete According to claim 1,
The operation of determining the candidate personality of the user,
displaying one or more question sentences;
receiving the user's response sentence corresponding to each of the one or more question sentences;
calculating a matching rate of words included in the user's response sentence matched to a word set for each personality type of the first personality type test; and
An operation of determining a candidate personality corresponding to a word set having the matching rate equal to or greater than a third threshold value.
Including, propensity-based matching method. According to claim 1,
The operation of determining the first user personality type of the user by performing the personality determination test,
displaying each of the expression sentences included in the sentence set corresponding to the candidate personality at regular time intervals, and receiving a selection input for each expression sentence;
summing scores of expression sentences selected by the selection input; and
determining the candidate personality as a first user personality type of the user when the result of the sum is equal to or greater than a fourth threshold;
Including, propensity-based matching method. According to claim 1,
The operation of determining the first user personality type of the user by performing the personality determination test,
displaying each of the expression sentences included in the sentence set corresponding to the candidate personality at regular time intervals, and receiving a selection input for each expression sentence; and
An operation of determining the first user personality type by inputting the selected one or more expression sentences into a personality type examination model composed of a neural network.
Including, propensity-based matching method. According to claim 7,
The personality type test model includes an input layer, one or more hidden layers, and an output layer, and the input layer outputs an identifier corresponding to the input expression sentence using a matching table storing a matching relationship between expression sentences and identifiers. and pass it to the next layer,
Each learning data composed of the learning expression sentence and the correct answer first user personality type is input to the input layer of the personality type examination model, passes through the one or more hidden layers and the output layer, and outputs an output vector, wherein the output vector is The input is input to a loss function layer connected to the output layer, and the loss function layer outputs a loss value using a loss function that compares the output vector with the correct answer vector for each learning data, and the parameter of the personality type test model is learned in the direction in which the loss value becomes smaller,
A propensity-based matching method. A computer program stored in a computer readable recording medium in order to execute the method of claim 1 in combination with hardware. A computer readable recording medium storing a computer program combined with hardware to execute the method of claim 1.

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