Electronic device and method for recommending similar products using same

Abstract

According to various embodiments, an electronic device may comprise: a display; a memory; and a processor operatively connected to the display and the memory. The processor can: identify a first product in response to a search request related to at least one product; identify identification information of a category related to the first product on the basis of an object embedding model; identify arrangement information of a plurality of objects within the identification information in which the plurality of objects are included; search for at least one second product related to the first product on the basis of the arrangement information of the plurality of objects; and output, on the display, a product related to the at least one retrieved second product. Various other embodiments may be possible.

Description

Electronic devices and methods for recommending similar products using the same

[0001] An embodiment of the present disclosure relates to an electronic device and a method for recommending similar products using the same.

[0002] With the growth of e-commerce (e.g., Internet services) such as online shopping malls and social commerce, users can search for products they want to purchase through online search environments, and it has become very common for them to purchase the products they find online.

[0003] Typically, when searching for a specific product on an online network, an electronic device can determine a search term by combining at least one of words, letters, and numbers referring to the specific product, and, based on a program and application related to the search, can search for at least one product related to the search term.

[0004] According to one embodiment, an electronic device can search for similar products (objects, goods) by combining appropriate methods based on a valid information level, and provides the searched similar products to a user. The method according to one embodiment can search for a specific product desired by a user and other objects (e.g., similar objects, recommended objects) with a high degree of similarity to the specific product, and provides the user with appropriate similar objects (e.g., similar products).

[0005] The above information may be provided as background information to aid in understanding the present disclosure. No claim or determination is made as to whether any of the above is prior art in connection with the present disclosure.

[0006] Typically, when searching for a specific product online, users can use a dedicated application program for purchasing products (e.g., products) or a search engine to find the desired item. For example, if a user uses the generic name of the product they want to purchase (e.g., words denoting a product group, such as computer, refrigerator, washing machine, air conditioner, TV) as a search term, multiple products may be retrieved based on that generic name. However, the number and type of products returned in search results can be so diverse that it can be difficult for users to select the appropriate product they desire.

[0007] Typically, when searching for a specific product, an electronic device may detect multiple products that are deemed similar to the specific product based on inputted letters, numbers, and symbols, and output the detected multiple products as results. The electronic device may perform a search using detailed information (e.g., metadata) contained in the specific product, or may perform a search based on inputted search terms (e.g., letters, numbers, and symbols).

[0008] If an electronic device doesn't perform a search based on specific information about the product the user wants (e.g., inventory information, model information, option information), it may be difficult to provide similar or recommended products tailored to the user's intent. For example, products within the same product family may have different classification systems and may store identification information (e.g., classification code information) and attribute information in different formats. Consequently, electronic devices may struggle to categorize products within the same product family, making it difficult to select and provide similar or recommended products tailored to the user's intent.

[0009] According to one embodiment, in response to a search request for a first product, an electronic device may verify identification information (e.g., classification code information) of a product group related to the first product, apply an object embedding model and provide recommended items based on a plurality of objects (e.g., code information composed of letters and numbers representing product-related features) associated with the identification information, and select at least one second product (e.g., a similar product) related to the first product. According to one embodiment, in the process of recommending a similar product (e.g., the second product), a plurality of similar objects associated with the identification information of the product group may be utilized, and a similar product (e.g., the second product) tailored to the user's intent may be provided to the user. Similar products selected tailored to the user's intent may be provided, and user convenience in product search may be improved.

[0010] Various embodiments of the present disclosure seek to provide an electronic device that recommends similar products related to a specific product according to a user's intention during the process of searching for a specific product.

[0011] The technical tasks to be achieved in this document are not limited to the technical tasks mentioned above, and other technical tasks not mentioned will be clearly understood by those with ordinary skill in the technical field to which this document pertains from the description below.

[0012] According to one embodiment, an electronic device may include a display, a memory, and a processor operatively connected to the display and the memory. The processor may, in response to a search request related to at least one product, identify a first product, identify identification information of a category related to the first product based on an object embedding model, identify arrangement information of a plurality of objects included in the identification information, and search for at least one second product related to the first product based on the arrangement information of the plurality of objects, and output information related to the searched at least one second product through the display.

[0013] In one embodiment, a method for recommending similar products in an electronic device may include, in response to a search request related to at least one product, an operation of confirming a first product, an operation of confirming identification information of a category related to the first product based on an object embedding model, an operation of confirming arrangement information of a plurality of objects included in the identification information, an operation of searching for at least one second product related to the first product based on the arrangement information of the plurality of objects, and an operation of outputting information related to the searched at least one second product through the display.

[0014] According to one embodiment, a non-transitory computer-readable storage medium (or, computer program product) storing one or more programs for executing a method for recommending similar products of an electronic device (101) may be described. According to one embodiment, the one or more programs may include instructions that, when executed by a processor of the electronic device, perform, in response to a search request related to at least one product, an operation of identifying a first product, an operation of identifying identification information of a category related to the first product based on an object embedding model, an operation of identifying arrangement information of a plurality of objects included in the identification information, an operation of searching for at least one second product related to the first product based on the arrangement information of the plurality of objects, and an operation of outputting information related to the searched for at least one second product through the display.

[0015] According to one embodiment, an electronic device may, in response to a search request for a first product, identify identification information of a product group related to the first product and apply an object embedding model based on a plurality of objects included in the identification information. Based on the object embedding model, the electronic device may select at least one second product (e.g., a similar product, a recommended product) related to the first product and provide information related to the at least one second product to the user. According to one embodiment, the electronic device may provide the user with at least one second product similar to the first product according to the user's intention, thereby improving user convenience related to product search.

[0016] According to one embodiment, an electronic device can use an object embedding model to more quickly select a second product (e.g., a similar product, a recommended product) and more efficiently provide a second product desired by a user.

[0017] The effects that can be obtained from the present disclosure are not limited to the effects mentioned above, and other effects that are not mentioned can be clearly understood by a person having ordinary skill in the art to which the present disclosure belongs from the description below.

[0018] In connection with the description of the drawings, the same or similar reference numerals may be used for the same or similar components.

[0019] FIG. 1 is a block diagram of an electronic device within a network environment according to one embodiment of the present disclosure.

[0020] FIG. 2 is an exemplary diagram illustrating a method for recommending similar products related to a specific product according to one embodiment of the present disclosure.

[0021] FIG. 3 is a block diagram of an electronic device according to one embodiment of the present disclosure.

[0022] FIG. 4 is a flowchart illustrating a method for recommending similar products according to one embodiment of the present disclosure.

[0023] FIG. 5 is an exemplary diagram illustrating each object included in identification information for a specific product according to one embodiment of the present disclosure.

[0024] FIG. 6 is an exemplary diagram illustrating a first method for calculating similarity between a first product and a second product based on first identification information of a first product and second identification information of a second product according to one embodiment of the present disclosure.

[0025] FIG. 7 is an exemplary diagram illustrating a second method for calculating similarity between a first product and a second product based on first identification information of a first product and second identification information of a second product according to one embodiment of the present disclosure.

[0026] FIG. 8 is an exemplary diagram illustrating a process for managing an object embedding model and a process for recommending similar products based on the object embedding model according to one embodiment of the present disclosure.

[0027] FIG. 1 is a block diagram of an electronic device (101) within a network environment (100) according to various embodiments. Referring to FIG. 1, in the network environment (100), the electronic device (101) may communicate with an electronic device (102) via a first network (198) (e.g., a short-range wireless communication network), or may communicate with at least one of an electronic device (104) or a server (108) via a second network (199) (e.g., a long-range wireless communication network). According to one embodiment, the electronic device (101) may communicate with the electronic device (104) via the server (108). According to one embodiment, the electronic device (101) may include 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), an interface (177), a connection terminal (178), a haptic module (179), a camera module (180), a power management module (188), a battery (189), a communication module (190), a subscriber identification module (196), or an antenna module (197). In some embodiments, the electronic device (101) may omit at least one of these components (e.g., the connection terminal (178)), or may have one or more other components added. In some embodiments, some of these components (e.g., the sensor module (176), the camera module (180), or the antenna module (197)) may be integrated into one component (e.g., the display module (160)).

[0028] The processor (120) may, for example, execute software (e.g., a program (140)) to control at least one other component (e.g., a hardware or software component) of the electronic device (101) connected to the processor (120) and perform various data processing or calculations. According to one embodiment, as at least a part of the data processing or calculations, the processor (120) may store commands or data received from other components (e.g., a sensor module (176) or a communication module (190)) in a volatile memory (132), process the commands or data stored in the volatile memory (132), and store result data in a non-volatile memory (134). According to one embodiment, the processor (120) may include a main processor (121) (e.g., a central processing unit or an application processor) or a secondary processor (123) (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor)) that can operate independently or together therewith. For example, if the electronic device (101) includes a main processor (121) and a secondary processor (123), the secondary processor (123) may be configured to use less power than the main processor (121) or to be specialized for a specified function. The secondary processor (123) may be implemented separately from the main processor (121) or as a part thereof.

[0029] The auxiliary processor (123) may control at least a portion of functions or states associated with at least one component of the electronic device (101) (e.g., a display module (160), a sensor module (176), or a communication module (190)), for example, on behalf of the main processor (121) while the main processor (121) is in an inactive (e.g., sleep) state, or together with the main processor (121) while the main processor (121) is in an active (e.g., application execution) state. According to one embodiment, the auxiliary processor (123) (e.g., an image signal processor or a communication processor (CP)) may be implemented as a part of another functionally related component (e.g., a camera module (180) or a communication module (190)). According to one embodiment, the auxiliary processor (123) (e.g., a neural network processing device) may include a hardware structure specialized for processing an artificial intelligence model. The artificial intelligence model may be generated through machine learning. Such learning may be performed, for example, in the electronic device (101) itself on which the artificial intelligence model is executed, or may be performed through a separate server (e.g., the server (108)). The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above. The artificial intelligence model may include a plurality of artificial neural network layers.The artificial neural network may be one of a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, or a combination of two or more of the above, but is not limited to the examples described above. In addition to, or alternatively to, a hardware structure, an artificial intelligence model may include a software structure.

[0030] The memory (130) can store various data used by at least one component (e.g., processor (120) or sensor module (176)) of the electronic device (101). The data can include, for example, software (e.g., program (140)) and input data or output data for commands related thereto. The memory (130) can include volatile memory (132) or non-volatile memory (134).

[0031] 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).

[0032] The input module (150) can receive commands or data to be used in a component of the electronic device (101) (e.g., a processor (120)) from an external source (e.g., a user) of the electronic device (101). The input module (150) can include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0033] The audio output module (155) can output audio signals to the outside of the electronic device (101). The audio output module (155) can include, for example, a speaker or a receiver. The speaker can be used for general purposes, such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. In one embodiment, the receiver can be implemented separately from the speaker or as part of the speaker.

[0034] The display module (160) can visually provide information to an external party (e.g., a user) of the electronic device (101). The display module (160) may include, for example, a display, a holographic device, or a projector and a control circuit for controlling the device. In one embodiment, the display module (160) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of a force generated by the touch.

[0035] The audio module (170) can convert sound into an electrical signal, or vice versa, convert an electrical signal into sound. According to one embodiment, the audio module (170) can acquire sound through the input module (150), output sound through the sound output module (155), or an external electronic device (e.g., electronic device (102)) (e.g., speaker or headphone) directly or wirelessly connected to the electronic device (101).

[0036] The sensor module (176) can detect the operating status (e.g., power or temperature) of the electronic device (101) or the external environmental status (e.g., user status) and generate an electrical signal or data value corresponding to the detected status. According to one embodiment, the sensor module (176) can include, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0037] The interface (177) may support one or more designated protocols that may be used to directly or wirelessly connect the electronic device (101) with an external electronic device (e.g., the electronic device (102)). In 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.

[0038] The connection terminal (178) may include a connector through which the electronic device (101) may be physically connected to an external electronic device (e.g., 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 (e.g., a headphone connector).

[0039] A haptic module (179) can convert electrical signals into mechanical stimuli (e.g., vibration or movement) or electrical stimuli that a user can perceive through tactile or kinesthetic sensations. In one embodiment, the haptic module (179) can include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

[0040] The camera module (180) can capture still images and videos. According to one embodiment, the camera module (180) may include one or more lenses, image sensors, image signal processors, or flashes.

[0041] The power management module (188) can manage power supplied to the electronic device (101). According to one embodiment, the power management module (188) can be implemented, for example, as at least a part of a power management integrated circuit (PMIC).

[0042] A battery (189) may power at least one component of the electronic device (101). In one embodiment, the battery (189) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0043] The communication module (190) may support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device (101) and an external electronic device (e.g., electronic device (102), electronic device (104), or server (108)), and the performance of communication through the established communication channel. The communication module (190) may operate independently from the processor (120) (e.g., application processor) and may include one or more communication processors (CPs) that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (190) may include a wireless communication module (192) (e.g., 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) (e.g., a local area network (LAN) communication module, or a power line communication module). Among these communication modules, the corresponding communication module can communicate with an external electronic device (104) via a first network (198) (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network (199) (e.g., a long-range communication network such as a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or WAN)). These various types of communication modules can be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module (192) can verify or authenticate the electronic device (101) within a communication network such as the first network (198) or the second network (199) by using subscriber information (e.g., an international mobile subscriber identity (IMSI)) stored in the subscriber identification module (196).

[0044] The wireless communication module (192) can support 5G networks and next-generation communication technologies following the 4G network, such as NR access technology (new radio access technology). The NR access technology can support high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and connection of multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low-latency communications)). The wireless communication module (192) can support, for example, a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate. The wireless communication module (192) can support various technologies for securing performance in a high-frequency band, such as beamforming, massive multiple-input and multiple-output (MIMO), full dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module (192) can support various requirements specified in the electronic device (101), an external electronic device (e.g., the electronic device (104)), or a network system (e.g., the second network (199)). According to one embodiment, the wireless communication module (192) can support a peak data rate (e.g., 20 Gbps or more) for eMBB realization, a loss coverage (e.g., 164 dB or less) for mMTC realization, or a U-plane latency (e.g., 0.5 ms or less for downlink (DL) and uplink (UL), or 1 ms or less for round trip) for URLLC realization.

[0045] The antenna module (197) can transmit or receive signals or power to or from an external device (e.g., 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 on a substrate (e.g., a PCB). According to one embodiment, the antenna module (197) may include a plurality of antennas (e.g., an array antenna, a first antenna, and a second antenna). For example, the first antenna may generate a first antenna signal in a first direction based on a linear polarization scheme, and the second antenna may generate a second antenna signal in a second direction different from the first direction based on a linear polarization scheme. For example, the first antenna signal and the second antenna signal may be implemented in a direction orthogonal to each other. If the first antenna signal is a communication signal in the x-axis direction, the second antenna signal may include a communication signal in the y-axis direction.

[0046] According to one embodiment, at least one antenna suitable for a communication method used in a communication network, such as a first network (198) or a second network (199), may be selected from the plurality of antennas, for example, by a communication module (190). A signal or power may be transmitted or received between the communication module (190) and an external electronic device via the selected at least one antenna. According to some embodiments, in addition to the radiator, another component (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as a part of the antenna module (197).

[0047] According to various embodiments, the antenna module (197) may form a mmWave antenna module. According to one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent a first side (e.g., a bottom side) of the printed circuit board and capable of supporting a designated high frequency band (e.g., a mmWave band), and a plurality of antennas (e.g., an array antenna) disposed on or adjacent a second side (e.g., a top side or a side side) of the printed circuit board and capable of transmitting or receiving signals in the designated high frequency band.

[0048] At least some of the above components can be interconnected and exchange signals (e.g., commands or data) with each other via a communication method between peripheral devices (e.g., a bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)).

[0049] According to one embodiment, commands or data may be transmitted or received between the electronic device (101) and an external electronic device (104) via a server (108) connected to a second network (199). Each of the external electronic devices (102 or 104) may be the same or a different type of device as the electronic device (101). According to one embodiment, all or part of the operations executed in the electronic device (101) may be executed in one or more of the external electronic devices (102, 104, or 108). For example, when the electronic device (101) is to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device (101) may, instead of or in addition to executing the function or service itself, request one or more external electronic devices to perform the function or at least a part of the service. One or more external electronic devices that receive the request may execute at least a portion of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device (101). The electronic device (101) may process the result as is or additionally and provide it as at least a portion of a response to the request. For this purpose, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used, for example. The electronic device (101) may provide an ultra-low latency service by using distributed computing or mobile edge computing, for example. In another embodiment, the external electronic device (104) may include an Internet of Things (IoT) device. The server (108) may be an intelligent server utilizing machine learning and / or a neural network. According to one embodiment, the external electronic device (104) or the server (108) may be included in the second network (199).The electronic device (101) can be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

[0050] FIG. 2 is an exemplary diagram illustrating a method for recommending similar products related to a specific product according to one embodiment of the present disclosure.

[0051] Referring to FIG. 2, an electronic device (e.g., the electronic device (101) of FIG. 1) may perform a similar product recommendation program (e.g., a similar product recommendation application, an application that performs a similar product recommendation function based on an object embedding model) for searching and extracting similar products. For example, the object embedding model may include a designated artificial intelligence model (e.g., an AI model). Referring to FIG. 2, a processor of the electronic device (101) (e.g., the processor (120) of FIG. 1) may apply identification information related to a first product (201) (e.g., a product that a user wishes to search for) to an object embedding model, and may search for at least one second product (221, 222, 223) (e.g., a similar product (202)) based on the object embedding model. For example, the processor (120) may encode identification information for the first product (201) based on the object embedding model to generate first code information. The processor (120) can generate second code information by encoding identification information for the second product (202) based on an object embedding model. The processor (120) can search for a second product (202) that is at least partially similar to the first product (201) by comparing the first code information and the second code information. The processor (120) can perform a product search (211) function for the first product (201), and can search for at least one second product (221, 222, 223) based on the object embedding model and provide the searched information to the user.

[0052] Referring to FIG. 2, the electronic device (101) can execute a similar product recommendation program (e.g., a similar product recommendation application, an application that performs a similar product recommendation function based on an object embedding model) installed in a memory (e.g., the memory (130) of FIG. 1) and can perform a product search (211) function for a first product (201). For example, in response to the performance of the product search (211) function, the electronic device (101) can select at least one second product (221, 222, 223) (e.g., a similar product group (202)) that is at least partially similar to the first product (201), and output and display the selected at least one second product (221, 222, 223) through a display (e.g., a display module (160) of FIG. 1). For example, the electronic device (101) may display a first product (201) and at least one second product (221, 222, 223) together through a user interface based on a similar product recommendation program.

[0053] According to one embodiment, the processor (120) can determine in detail which product group the first product (201) belongs to and what type of product it is based on an object embedding model provided by a similar product recommendation program. According to one embodiment, the object embedding model can classify a plurality of product groups according to set criteria based on a neural network having a multi-layer structure (e.g., a deep neural network (DNN)) and can manage at least one similar product belonging to the same product group. For example, the object embedding model can be included in a machine learning technique, can learn based on the user's empirical data, and can perform predictions to improve its own performance. For example, the object embedding model can include a designated artificial intelligence model (e.g., an AI model). The object embedding model can be updated based on the execution history of a similar product recommendation program, the search history, and the user's selection history related to the result value (e.g., similar product). For example, the object embedding model can be individually managed for each account (e.g., user). The processor (120) can encode identification information for a specific product based on the object embedding model to generate code information corresponding to the specific product. For example, the code information can include classification code information in which a plurality of objects (e.g., letters, numbers) are arranged according to set positions. According to one embodiment, the code information may include position information (e.g., location information, arrangement information) for each object within a plurality of objects, and characteristic and identification information for each object.

[0054] According to one embodiment, the processor (120) may search for at least one second product (221, 222, 223) that belongs to the same product group as the first product (201) and has at least partial similarity to the first product (201) based on an object embedding model, and may output the at least one second product (221, 222, 223) as a similar product to the first product (201). For example, the electronic device (101) may display the at least one second product (221, 222, 223) in a user interface (UI) of a similar product recommendation program through the display (160). When a user searches for a first product (201), at least one second product (221, 222, 223) can be output as recommended products (e.g., similar products) related to the first product (201) and provided to the user.

[0055] Referring to FIG. 2, the first product (201) may include a portable terminal device such as a smartphone. In response to a product search (211) for the first product (201), the electronic device (101) may select at least one second product (221, 222, 223) that is at least partially related to the first product (201). For example, a second product group (202) including at least one second product may differ from the first product (201) in at least one of color, shape, version, form factor, specification, function, weight, price, and stock status. For example, the first code information for the first product (201) and the second code information for the second product (202) may differ at least partially from each other. According to one embodiment, in response to a product search (211) for a first product (201), the electronic device (101) may determine priorities among a plurality of second products having remaining stock according to an object embedding model, and select at least one second product (221, 222, 223) in order of relatively high priority. For example, the electronic device (101) may display a second product having a relatively high priority at the top of a list of similar products (e.g., recommended products), or may display the second product by applying a highlight effect.

[0056] FIG. 3 is a block diagram of an electronic device according to one embodiment of the present disclosure.

[0057] The electronic device (101) of FIG. 3 (e.g., the electronic device (101) of FIG. 1) may be at least partially similar to the electronic device (101) of FIG. 1, or may further include other embodiments of the electronic device. For example, the electronic device (101) may support a function of searching for and displaying a first product (e.g., the first product (201) of FIG. 2) and at least one second product (e.g., the second product group (202) of FIG. 2) related to the first product (201) based on an object embedding model, in response to execution of a similar product recommendation program (e.g., an application program). For example, the electronic device (101) may encode identification information for the first product (201) based on the object embedding model to generate first code information, and may encode identification information for the second product (201) to generate second code information. The electronic device (101) can search for a second product (202) that is at least partially similar to the first product (201) by comparing the first code information and the second code information. According to one embodiment, the electronic device (101) can display the first product (201) and at least one second product (221, 222, 223) (e.g., similar products, recommended products) based on a user interface according to a similar product recommendation program.

[0058] According to one embodiment, the processor (120) may search for at least one second product (221, 222, 223) that belongs to the same product group as the first product (201) and has at least partial similarity to the first product (201) based on an object embedding model, and may output the at least one second product (221, 222, 223) as a similar product to the first product (201). For example, the electronic device (101) may display the first product (201) and at least one second product (221, 222, 223) in a user interface (UI) of a similar product recommendation program through a display (e.g., a display module (160) of FIG. 1). According to one embodiment, when a user searches for a first product (201), the electronic device (101) may output and provide to the user, through the display (160), at least one second product (221, 222, 223) as recommended products (e.g., similar products) related to the first product (201).

[0059] Referring to FIG. 3, the electronic device (101) may include a processor (e.g., the processor (120) of FIG. 1), a memory (e.g., the memory (130) of FIG. 1), a display (e.g., the display module (160) of FIG. 1), and / or a communication circuit (390) (e.g., the communication module (190) of FIG. 1). In various embodiments, the electronic device (101) may include additional components in addition to the components illustrated in FIG. 3, or may omit at least one of the components illustrated in FIG. 3. For example, the processor (120) of the electronic device (101) may be operatively, functionally, and / or electrically connected to the memory (130), the display (160), and the communication circuit (390).

[0060] According to one embodiment, the processor (120) of the electronic device (101) may execute a program (e.g., program (140) of FIG. 1) stored in the memory (130) to control at least one other component (e.g., hardware or software component) and perform various data processing or calculations. For example, the processor (120) may support a function of searching for and displaying a first product (201) and at least one second product (202) related to the first product (201) based on an object embedding model in response to the execution of a similar product recommendation program (e.g., application program). The processor (120) can select a second product (202) based on at least one object information (311) (e.g., code information implemented using an object embedding model) included in the identification information (302) of the first product (201), and can display the selected second product (202) through the display (160).

[0061] In one embodiment, the memory (130) may store a similar product recommendation program (e.g., an application program) and an object embedding model for recommending similar products. In one embodiment, the memory (130) may store data and / or instructions received from or generated by other components (e.g., a processor (120), a display module (160), or a communication circuit (390)). In one embodiment, the processor (120) may use the object embedding model stored in the memory (130) to search for a first product (201) and at least one second product (202) similar to the first product (201).

[0062] According to one embodiment, the object embedding model can select a first product (201) and a second product (202) that is at least partially related to the first product (201) based on category information (301) and identification information (302). For example, the category information (301) can be defined as a product group indicating a type and kind of a specific product. For example, the category information (301) can include information that generally indicates a type of electronic device (101), such as a TV, a refrigerator, a washing machine, or a dryer. For example, the identification information (302) can include a product name or model name indicating each product. The identification information (302) can include object information (311) indicating at least one of color, shape, version, form factor, specification, function, weight, price, and stock status for each product. According to one embodiment, the processor (120) may encode (encode) identification information (302) of a specific product based on an object embedding model, and generate code information (e.g., classification code information composed of multiple objects) corresponding to the specific product. For example, first code information for a first product (201) and second code information for a second product (202) may be at least partially different.

[0063] According to one embodiment, the object embedding model may select a first product (201) and a second product (202) that is at least partially related to the first product (201) based on a description of the product and text information about the product. For example, when a description (e.g., text) and features related to a specific product are input, the object embedding model may generate identification information (302) (e.g., object information (311)) corresponding to the specific product based on the input description and features. For example, the processor (120) may generate first code information corresponding to the first product (201) based on the description and features related to the first product (201). As another example, the processor (120) may generate second code information corresponding to the second product (202) based on the description and features related to the second product (202). According to one embodiment, the processor (120) can select at least one second product (202) that is at least partially similar to the first product (201) using first code information and second code information generated based on an object embedding model.

[0064] According to one embodiment, the object embedding model may generate identification information (302) (e.g., object information (311)) corresponding to a specific product by combining at least one or more of category information (301), identification information (302), description of the product, and text information of the product. For example, when a search request for a specific product is received, the processor (120) may apply the description of the product, text information of the product, category information of the product, and identification information of the product according to the search request to the object embedding model, and generate identification information corresponding to the specific product based on the object embedding model.

[0065] According to one embodiment, the processor (120) can identify the first product (201) based on at least one of a description, text information, category information, and identification information corresponding to at least one product according to a search request.

[0066] According to one embodiment, in response to a product search request for a first product (201), the processor (120) may select at least one second product (202) that is at least partially similar to the first product (201) based on category information (301) and identification information (302). For example, the processor (120) may identify identification information (302) corresponding to the first product (201) and identify object information (311) for each of a plurality of objects included in the identification information (302). The object information (311) may include arrangement information (e.g., location information, position information) for each object within the identification information (302). For example, the object information (311) may include arrangement information for a plurality of objects included within the identification information (302). The arrangement information may include information related to the arrangement position, arrangement order, and arrangement order of the objects for a plurality of objects constituting the identification information (302). According to one embodiment, the processor (120) may search for at least one second product (202) that is at least partially related to the first product (201) based on the arrangement information (e.g., arrangement position, arrangement order, arrangement order) for each object included in the identification information of the first product (201). According to one embodiment, the processor (120) may provide the first product (201) and at least one second product (202) together to the user.

[0067] According to one embodiment, the processor (120) may convert processed image signals, data signals, control signals, etc. based on an object embedding model, and display information related to the processed signals through a display (160). For example, the processor (120) may display a first product (201) and at least one second product (202) that is at least partially related to the first product (201) based on a user interface generated by a similar product recommendation program (e.g., an application program).

[0068] According to one embodiment, the processor (120) may establish a direct (e.g., wired) communication channel or a wireless communication channel with an external electronic device (e.g., electronic device (102), electronic device (104), or server (108)) through a communication circuit (390) (e.g., communication module (190) of FIG. 1), and perform network communication through the established communication channel.

[0069] According to one embodiment, the processor (120) of the electronic device (101) may, in the process of searching for a similar product group (e.g., a second product (202), at least one second product (221, 222, 222)) for the first product (201), check category information (301) (e.g., product group, product type, product type) related to the first product (201) and check identification information (302) corresponding to the checked category information (301). For example, the identification information (302) may include metadata information and attribute information of the corresponding product (e.g., the first product (201)). For example, a first product (201) and a second product (202) (e.g., at least one second product (221, 222, 223)) belonging to the same category (e.g., mobile terminal, smart phone) may include at least partially identical identification information (302). For example, the processor (120) may encode identification information for the first product (201) based on an object embedding model to generate first code information. The processor (120) may encode identification information for the second product (202) based on an object embedding model to generate second code information. The processor (120) may compare the first code information and the second code information to select the second code information that is at least partially similar to the first code information. According to one embodiment, the processor (120) can search for at least one second product (221, 222, 222) based on first identification information for the first product (201).

[0070] According to one embodiment, the processor (120) can confirm first identification information corresponding to the first product (201), and based on the confirmed first identification information, select at least one second product (221, 222, 223) belonging to the same category (e.g., product group). For example, at least one second product (221, 222, 223) may each include second identification information. The first identification information for the first product (201) and the second identification information for each of the second products (221, 222, 223) may include at least partially the same object (e.g., object information (311)).

[0071] According to one embodiment, the identification information (302) may include a plurality of objects (e.g., objects, object information (311)) and may be implemented in a form that represents arrangement information for each of the plurality of objects. For example, the object information (311) included in the identification information (302) may include at least one character and at least one number, and may include a code value (e.g., code information, classification code information) indicating a characteristic of the product. The first product (201) may include first identification information and first code information corresponding to the first identification information, and the second product (202) may include second identification information and second code information corresponding to the second identification information. According to one embodiment, the object information (311) included in the identification information (320) may be set to include at least one of at least one character, at least one number, and at least one symbol. The object information (311) may include arrangement information for at least one of at least one character, at least one number, and at least one symbol.

[0072] According to one embodiment, the processor (120) may apply arrangement information for each object included in the first object information of the first product (201) to an object embedding model, and select at least one second product (202) having a relevance to the first product (201). For example, the processor (120) may select second object information (e.g., second code information) that is at least partially different from the first object information (e.g., first code information) based on the arrangement information for each object included in the first object information. The processor (120) may calculate a similarity between the first product (201) and at least one second product (202), and select at least one second product (202) based on the calculated similarity. For example, at least one second product (202) may include products of the same product family that have a certain level of similarity (e.g., a set threshold) with respect to the first product (201).

[0073] According to one embodiment, the processor (120) may set weights for object information (311) and operate an object embedding model to which weights are applied. When selecting at least one second product (202), the processor (120) may apply a relatively high weight to an object with high importance (e.g., at least one of color, shape, version, form factor, specification, function, weight, price, and stock status). For example, when a user searches for a first product (201), if the user wants a second product (202) that is in the same product family but has a different form factor, the processor (120) may set a relatively high weight to be applied to an object indicating a “form factor” (e.g., letters, numbers). As another example, when a user searches for a first product (201), if the user wants a second product (202) that is in the same product family but supports a specific function, the processor (120) may set a relatively high weight to be applied to an object indicating the “specific function.” According to one embodiment, the processor (120) can set different weights corresponding to each object included in the object information (311).

[0074] According to one embodiment, each object comprising a plurality of objects may include at least one of category information, type information, option information, function information, color information, shape information, manufacturing date information, and manufacturing country information related to the corresponding product. According to one embodiment, the information represented by each object is not limited to the aforementioned information and may be variously set by the manufacturer and developer. According to one embodiment, the number of objects constituting the object information (311) is not limited to a specific number.

[0075] According to one embodiment, the processor (120) may compare first code information of a first product (201) and second code information of a second product (202), and may calculate a similarity of a second product (202) based on the first product (201) based on code information that at least partially matches among the first code information and the second code information. For example, the similarity of the second product (202) may be information that quantifies how similar the second product (202) is based on the first product (201). The similarity of the second product (202) may be information that quantifies the relevance of the second product (202) based on the first product (201) in a state that matches a criterion desired by a user (e.g., a state in which weights are reflected for each object). According to one embodiment, the processor (120) can apply weights set to the first code information and the second code information, and can also calculate the similarity of the state to which the weights are applied.

[0076] According to one embodiment, the processor (120) may select a second product (202) that is at least partially related to a first product (201) based on an object embedding model, and may provide the first product (201) and the selected second product (202) to the user. For example, the user may select a product having a relatively high level of interest among the first product (201) and the second product (202). According to one embodiment, the processor (120) may confirm a product selected by the user among similar products (e.g., the second product (202)) recommended based on the object embedding model, and may update the object embedding model using information related to the similar product selected by the user. For example, if it is determined that the user preferentially selects a product with a different form factor from among the recommended similar products, the processor (120) may update the object embedding model so that, when searching for the first product (201), the second product (202) with a different form factor from the first product (201) is displayed relatively first.

[0077] According to one embodiment, an electronic device (e.g., an electronic device (101) of FIG. 3) may include a display (e.g., a display module (160) of FIG. 1, a display (160) of FIG. 3), a memory (e.g., a memory (130) of FIGS. 1 and 3), and a processor (e.g., a processor (120) of FIGS. 1 and 3) operatively connected to the display (160) and the memory (130). According to one embodiment, the processor (120) may identify a first product in response to a search request related to at least one product. The processor (120) may identify identification information (e.g., identification information (302) of FIG. 3) of a category related to the first product (e.g., the first product (201) of FIG. 2) based on an object embedding model. The processor (120) may identify arrangement information of a plurality of objects within the identification information (302) for a plurality of objects included in the identification information (302). The processor (120) can search for at least one second product (202) related to the first product (201) based on the arrangement information of a plurality of objects. The processor (120) can output information related to the searched at least one second product (202) through the display (160).

[0078] According to one embodiment, the processor (120) may calculate a similarity corresponding to at least one second product (202) based on arrangement information of a plurality of objects. The processor (120) may search for at least one second product (202) based on the calculated similarity.

[0079] According to one embodiment, each object included in the identification information (302) may be set to include at least one letter, at least one number, and at least one symbol.

[0080] According to one embodiment, the processor (120) may set a weight corresponding to each object included in the identification information (302). In response to a situation where the first code information corresponding to the first product (201) and the second code information corresponding to the second product (202) at least partially match each other, the processor (120) may apply the weight set corresponding to the matched code information.

[0081] According to one embodiment, each object constituting a plurality of objects may include information indicating at least one of product category information, product type information, product option information, product function information, product color information, product manufacturing date information, and product manufacturing country information related to at least one product.

[0082] According to one embodiment, if a first object is not included among a plurality of objects, the processor (120) may identify a second object related to the first object based on an object embedding model. Based on the object embedding model, the processor (120) may search for at least one second product (202) that at least partially matches the identified second object.

[0083] According to one embodiment, the processor (120) can determine a weight set for each object based on the arrangement information. The processor (120) can apply the weight set to each object among multiple objects. The processor (120) can search for at least one second product (202) based on the similarity according to the set weight.

[0084] According to one embodiment, the processor (120) may update an object embedding model based on the at least one second product (202) in response to information relating to the at least one second product (202) being output.

[0085] According to one embodiment, the electronic device (101) may further include a communication circuit (190) for communicating with an external electronic device (e.g., the server (108) of FIG. 1). The processor (120) may transmit product information related to the first product (201) to the external electronic device (108) through the communication circuit (190). The processor (120) may receive at least one second product (202) related to the first product (201), which is searched based on the object embedding model of the external electronic device (108). The processor (120) may output information related to the received at least one second product (202).

[0086] According to one embodiment, the processor (120) can identify the first product (201) based on at least one of a description, text information, category information, and identification information corresponding to the at least one product according to a search request.

[0087] FIG. 4 is a flowchart illustrating a method for recommending similar products according to one embodiment of the present disclosure.

[0088] In the following examples, the operations may be performed sequentially, but are not necessarily sequential. For example, the order of the operations may be changed, and at least two operations may be performed in parallel.

[0089] According to one embodiment, operations 401 to 411 may be understood to be performed in a processor (e.g., processor (120) of FIGS. 1 and 3) of an electronic device (e.g., electronic device (101) of FIGS. 1 and 3).

[0090] The electronic device (101) of FIG. 4 may be at least partially similar to the electronic device (101) of FIG. 3, or may further include other embodiments of the electronic device (101).

[0091] According to one embodiment, the electronic device (101) may have a similar product recommendation program (e.g., a similar product recommendation application, an application that performs a similar product recommendation function based on an object embedding model) stored in a memory (e.g., a memory (130) of FIG. 3). When the processor (e.g., the processor (120) of FIG. 3) of the electronic device (101) performs a product search function (e.g., a product search function for a first product (e.g., a first product (201) of FIG. 2)) based on the similar product recommendation program, the processor (e.g., the processor (120) of FIG. 3) may search for the first product (201) and a second product (e.g., a second product (202) of FIG. 2, at least one second product (221, 222, 223), a similar product group) that is at least partially similar to the first product (201) based on the object embedding model. For example, the processor (120) may encode identification information for the first product (201) based on an object embedding model to generate first code information. The processor (120) may encode identification information for the second product (202) based on the object embedding model to generate second code information. The processor (120) may search for a second product (202) that is at least partially similar to the first product (201) by comparing the first code information and the second code information. According to one embodiment, when a product search function for the first product (201) is performed at the request of a user, the electronic device (101) may select the first product (201) and the second product (202) that is at least partially similar to the first product (201) based on an object embedding model, and provide the first product (201) and the second product (202) that are at least partially similar to the first product (201) to the user. The electronic device (101) may display a first product (201) and a second product (202) that is at least partially similar to the first product (201) based on a user interface of a similar product recommendation program.

[0092] In operation 401, the processor (120) can identify a first product (201) according to a product search request (e.g., product search (211) of FIG. 2). For example, the electronic device (101) can identify a search request signal for the first product (201) and at least one second product (202) related to the first product (201) while a similar product recommendation program is running. The processor (120) can identify the first product (201) for which a product search has been requested.

[0093] In operation 403, the processor (120) may check identification information (e.g., identification information (302) of FIG. 3) of a product group (e.g., product type, category information (301) of FIG. 3) related to the first product (201) based on the object embedding model. For example, the first product (201) may include category information (301) to which the first product (201) belongs and identification information (302) indicating detailed specifications (e.g., function) of the first product (201). In response to a product search request for the first product (201), the processor (120) may check identification information (302) corresponding to the first product (201). For example, the processor (120) may generate first code information corresponding to the first product (201) based on the object embedding model.

[0094] In operation 405, the processor (120) can check a plurality of objects (e.g., object information (311) of FIG. 3) included in the identification information (302). For example, the identification information (302) can be composed of a plurality of objects indicating at least one of color, shape, version, form factor, specification, function, weight, price, and stock status of the product. The identification information (302) indicates detailed information about the product and can be defined as a product name or model name. The processor (120) can code (encode) the identification information (320) of the first product (201) and generate first code information (e.g., classification code information composed of a plurality of objects) corresponding to the first product (201).

[0095] In operation 407, the processor (120) can check arrangement information for each of a plurality of objects within the identification information (302) (e.g., code information, first code information). For example, each object included in the identification information (302) may have a storage location and a storage order preset. For example, within the identification information (302), a first object sequentially located first and a second object sequentially located second may represent a product group (e.g., category information (301)) of the first product (201). For example, the processor (120) can check a product group for the first product (201) based on the first object and the second object within the identification information (302).

[0096] In operation 409, the processor (120) may apply arrangement information for each of a plurality of objects to an object embedding model. For example, the processor (120) may apply first identification information of a first product (201), which includes arrangement information for each object, to an object embedding model, and based on the object embedding model, may search for at least one second product (202) that is at least partially related to the first product (201).

[0097] In operation 411, the processor (120) may search for a second product (202) related to the first product (201). For example, the processor (120) may calculate a similarity between the first product (201) and the second product (202) based on first identification information (e.g., first code information) of the first product (201) and second identification information (e.g., second code information) of the second product (202), and may determine, based on the similarity, how closely the second product (202) is related to the first product (201). According to one embodiment, the processor (120) may select the second products (202) in order of relatively high similarity, and display the selected second products (202).

[0098] According to one embodiment, the electronic device (101) can set a weight corresponding to each object constituting identification information (e.g., code information), and when calculating the similarity, the set weight can be applied corresponding to each object. For example, when the user has a high interest in various form factors for the first product (201), the electronic device (101) can set a relatively high weight to be applied to an object representing the form factor. According to one embodiment, in the process of calculating the similarity, the electronic device (101) can apply a weight corresponding to each object, and when the weight is applied, the similarity between the first product (201) and the second product (202) can be calculated. The electronic device (101) can output and display the second product (202) having a relatively high similarity.

[0099] According to one embodiment, the electronic device (101) can identify a first product in response to a product search request and, based on the identification information of the first product, determine whether the first product is in stock. For example, if the first product is out of stock, the electronic device (101) can search for at least one second product that has identification information (e.g., code information) that at least partially matches the identification information of the first product and that has stock.

[0100] FIG. 5 is an exemplary diagram illustrating each object included in identification information for a specific product according to one embodiment of the present disclosure.

[0101] In the description related to FIG. 5, the electronic device (101) may be at least partially similar to the electronic device (101) of FIG. 3, or may further include other embodiments of the electronic device (101).

[0102] Referring to FIG. 5, a processor of an electronic device (101) (e.g., a processor (120) of FIG. 3) can check identification information (511) (e.g., identification information (302) of FIG. 3) for a specific product (e.g., a first product (201) of FIG. 2) and code information (521) implemented by encoding the identification information (511). The processor (120) can input the identification information (511) into a coding function (501) and check code information (521) representing the identification information (511). For example, the identification information (511) may be in a form in which at least one letter and at least one number are combined and arranged. Referring to FIG. 5, the identification information (511) of the first product (201) may be in the form of “AF19BX890EFN” in which a total of 12 letters and numbers are combined.

[0103] According to one embodiment, the processor (120) may encode (encode) identification information (302) of a specific product based on an object embedding model, and generate code information (e.g., classification code information composed of multiple objects) corresponding to the specific product. For example, the processor (120) may encode (encode) identification information (511) of a first product (201) based on an object embedding model, and implement the identification information (511) as the code information (521).

[0104] Referring to FIG. 5, the processor (120) can apply identification information (511) based on the function formula attached to (Table 1) below, and generate code information (521) in the form of a combination of at least one character and at least one number.

[0105] Let a character C at the position PP be P_ENC(C) = PP: 'C'If C is the first character, add ^ at the frontLet P_ENC(model_code)=P_ENC(C1,C2,C3…CN)=”^P_ENC(C1), P_ENC(C2), P_ENC(C3),…P_ENC(CN)

[0106] According to one embodiment, the functional formula of (Table 1) can be utilized to encode identification information (511) and generate code information (521). According to one embodiment, in generating the code information (521), various functional formulas and mathematical formulas can be applied, and are not limited to the functional formula of (Table 1).

[0107] Referring to FIG. 5, the code information (521) may include each object (541, 542, 543) constituting the identification information (511) (e.g., “AF19BX890EFN”) and the arrangement information (531, 532, 533) of the objects. For example, the first object of the code information (521) illustrated in FIG. 5 may be the letter “A” (541), and the first arrangement information (531) of the letter “A” (541) may be “^00”. “^” may be a symbol indicating the first object. The second object of the code information (521) may be the letter “F” (542), and the second arrangement information (532) of the letter “F” (542) may be “01”. The third object of the code information (521) may be the number “1” (543), and the third arrangement information (533) of the number “1” (543) may be “02”. According to one embodiment, the code information (521) may include arrangement information (531, 532, 533) for objects included in the identification information (511) and object information (541, 542, 543) for the objects, and may be implemented in a form arranged according to the order of the objects. According to one embodiment, the code information (521) is not limited to the form illustrated in FIG. 5, and may be implemented in various forms.

[0108] According to one embodiment, the processor (120) may assign a weight to each of the objects constituting the code information (521). For example, if the weight assigned to the first object is relatively greater than the weight assigned to the second object, the processor (120) may increase the number of characters included in the first object. The processor (120) may convert the first object, the character “A” (541), into “AAAA” and apply the weight set to the first object. For example, if the second object, the character “F” (542), is converted into “FF”, it may mean that the first object (“AAAA”) has a relatively higher importance (e.g., priority) than the second object (“FF”). According to one embodiment, the processor (120) may assign different weights to each of the objects constituting the code information (521), and determine the number of characters for each object based on the assigned weights. According to one embodiment, the processor (120) can select a second product (202) that is at least partially similar to the first product (201) based on the weighted code information (521).

[0109] FIG. 6 is an exemplary diagram illustrating a first method for calculating a similarity between a first product and a second product based on first identification information of the first product and second identification information of the second product according to one embodiment of the present disclosure. FIG. 7 is an exemplary diagram illustrating a second method for calculating a similarity between a first product and a second product based on first identification information of the first product and second identification information of the second product according to one embodiment of the present disclosure.

[0110] In the descriptions related to FIGS. 6 and 7, the electronic device (101) may be at least partially similar to the electronic device (101) of FIG. 3, or may further include other embodiments of the electronic device (101).

[0111] Referring to FIG. 6, a processor of an electronic device (101) (e.g., processor (120) of FIG. 3) can compare first identification information (611) of a first product (e.g., first product (201) of FIG. 2) and second identification information (612) of a second product (e.g., second product (202) of FIG. 2). For example, the first identification information (611) may be in the form of “UA55RU7250KXXV” in which a total of 14 letters and numbers are combined, and the second identification information (612) may be in the form of “UA65RU9000KXXV” in which a total of 14 letters and numbers are combined. For example, the first identification information (611) may be implemented in a form in which the first object to the 14th object are sequentially listed. The first object may be the letter “U” and the fourteenth object may be the letter “V”. The first identification information (611) and the second identification information (612) illustrated in FIG. 6 are determined by way of example and are not limited thereto.

[0112] Referring to FIG. 6, for the first identification information (611), the first object may be the letter “U” and the second object may be the letter “A”. Referring to FIG. 6, for the second identification information (612), the first object may be the letter “U” and the second object may be the letter “A”. For example, the first object and the second object included in the identification information may be defined as “product group information” for a product. The first product group information (621) of the first identification information (611) may be “UA”, and the second product group information (622) of the second identification information (612) may be “UA”. The first product (201) and the second product (202) may be products belonging to the same product group (e.g., the category information (301) of FIG. 3).

[0113] Referring to FIG. 6, the first identification information (611) may be determined based on at least one character and at least one number. For example, the first array structure (631) of the first identification information (611) may have the structure “AANNAANNNNAAAA”, and the second array structure (632) of the second identification information (612) may have the structure “AANNAANNNNAAAA”. The first identification information (611) and the second identification information (612) may be implemented to have substantially the same structure.

[0114] Referring to FIG. 6, the processor (120) can compare the first identification information (611) of the first product (201) and the second identification information (612) of the second product (202) in various ways, and can calculate the similarity between the first product (201) and the second product (202). The processor (120) can compare the first product group information (621) of the first identification information (611) and the second product group information (622) of the second identification information (612) with the first matching condition (641) (prefix match), and can confirm that the first product (201) and the second product (202) correspond to substantially the same product group. The processor (120) can compare the first array structure (631) of the first identification information (611) and the second array structure (632) of the second identification information (612) with the second matching condition (642) (ANS match). For example, the first identification information (611) and the second identification information (612) are implemented with a total of 14 objects and can have substantially the same array structure. The processor (120) can confirm that the matching rate for the second matching condition (642) is approximately 100%. The processor (120) can confirm the relevance between the first product (201) and the second product (202) with the third matching condition (643) (CHAR match). For example, the processor (120) can confirm that the matching rate between the object included in the first identification information (611) and the object included in the second identification information (612) is about 78.6%. The processor (120) can confirm that a total of 11 objects out of a total of 14 objects constituting the first identification information (611) and the second identification information (612) match each other. The processor (120) can set a threshold value in relation to the third matching condition (643), and in response to a condition in which the matching rate exceeds the set threshold value, can calculate the similarity between the first product (201) and the second product (202).

[0115] According to one embodiment, the processor (120) may set weights for the second matching condition (642) and the third matching condition (643) when calculating the similarity, and may calculate the similarity between the first product (201) and the second product (202) by reflecting the set weights. Referring to FIG. 6, a weight of about 0.3 is set for the second matching condition (642), and a weight of about 0.7 is set for the third matching condition (643), but the present invention is not limited thereto. According to one embodiment, the processor (120) may apply a weight of about 0.3 to a matching rate of about 100% according to the second matching condition (642), and may apply a weight of about 0.7 to a matching rate of about 78.6% according to the third matching condition (643), based on the first similarity calculation formula (650). Referring to FIG. 6, the processor (120) can confirm that the similarity between the first product (201) and the second product (202) is approximately 85%. For example, a high similarity may mean that the relevance between the first product (201) and the second product (202) is high.

[0116] According to one embodiment, the processor (120) may, in response to a product search request for a first product (201), select at least one second product (202) and calculate a similarity between the first product (201) and at least one second product (202). The processor (120) may determine a priority for the at least one second product (202) based on the calculated similarity. For example, the higher the similarity between the first product (201) and the second product (202), the higher the priority of the second product (202). According to one embodiment, when selecting similar products or recommended products for the first product (201), the processor (120) may preferentially display the second product (202) having a relatively high priority. The processor (120) can select a second product (202) having a relatively high degree of similarity based on the first product (201), and display the selected second product through a display (e.g., the display (160) of FIG. 3).

[0117] According to one embodiment, in response to a product search request for a first product (201), the electronic device (101) may provide the user with products having a high degree of similarity to the first product (201) with priority.

[0118] Referring to FIG. 7, the processor (120) of the electronic device (101) can compare the first identification information (711) of the first product (201) and the second identification information (712) of the second product (202). For example, the first identification information (711) may be in the form of “UA55RU7250KXXV” with a total of 14 letters and numbers combined, and the second identification information (712) may be in the form of “UA65AU9000KXXV” with a total of 14 letters and numbers combined. The first identification information (711) and the second identification information (712) may be implemented in a form in which the first object to the 14th object are sequentially listed. The first identification information (711) and the second identification information (712) may each include a total of 14 objects.

[0119] Referring to FIG. 7, the processor (120) can check the third matching condition (713) (CHAR match) between the first identification information (711) and the second identification information (712). The processor (120) can individually set different weights (PA weights) for each object. For example, a weight of approximately 0.05 can be set for the first object (“U”) and the second object (“A”), and a weight of approximately 0.1 can be set for the third object and the fourth object.

[0120] According to one embodiment, the processor (120) may, when calculating the similarity, check the weight set corresponding to each object based on the third matching condition (643), and by reflecting the set weight to each object, the similarity between the first product (201) and the second product (202) may be calculated. Referring to FIG. 7, a relatively high weight is reflected for a specific object, which may mean that similar products and recommended products are selected based on object information with a high weight. For example, if a user is interested in similar products that are the same size as the product, the weight for object information indicating the size of the product may be set relatively high, and when searching for similar products, similar products with the same size as the size of the first product may be selected. For example, the fact that a high weight is set for a specific object may mean that the user has set the characteristics of the product corresponding to the specific object as a main search condition.

[0121] According to one embodiment, the processor (120) may, in response to a product search request for a first product (201), select at least one second product (202) and calculate a similarity between the first product (201) and at least one second product (202). When calculating the similarity, the processor (120) may set different weights for each object. For example, a relatively high weight for a specific object may mean that similar products and recommended products are selected by giving priority to features and functions corresponding to the specific object. The processor (120) may determine a priority for at least one second product (202) (e.g., similar products, recommended products) based on the calculated similarity. For example, the higher the similarity between the first product (201) and the second product (202), the higher the priority of the second product (202).

[0122] According to one embodiment, the processor (120) may, when selecting similar products or recommended products for the first product (201), preferentially display a second product (202) having a relatively high priority. The processor (120) may select a second product (202) having a relatively high degree of similarity based on the first product (201), and may display the selected second product through a display (e.g., the display (160) of FIG. 3).

[0123] FIG. 8 is an exemplary diagram illustrating a process for managing an object embedding model and a process for recommending similar products based on the object embedding model according to one embodiment of the present disclosure.

[0124] In the description related to FIG. 8, the electronic device (101) may be at least partially similar to the electronic device (101) of FIG. 3, or may further include other embodiments of the electronic device (101).

[0125] According to one embodiment, the electronic device (101) may have a similar product recommendation program (e.g., a similar product recommendation application, an application that performs a similar product recommendation function based on an object embedding model) stored in a memory (e.g., a memory (130) of FIG. 3). When the processor (e.g., the processor (120) of FIG. 3) of the electronic device (101) performs a product search function (e.g., a product search function for a first product (e.g., a first product (201) of FIG. 2)) based on the similar product recommendation program, the processor (e.g., the processor (120) of FIG. 3) may search for the first product (201) and a second product (e.g., a second product (202) of FIG. 2, at least one second product (221, 222, 223), a similar product group) that is at least partially similar to the first product (201) based on the object embedding model. According to one embodiment, when a product search function for a first product (201) is performed at the request of a user, the electronic device (101) may compare and analyze first code information corresponding to the first product (201) generated based on an object embedding model and second code information corresponding to the second product (202) generated based on the object embedding model. The electronic device (101) may check the first code information of the first product (201) and second code information at least partially similar to the first code information, select the second product (202) corresponding to the second code information, and provide the selected result to the user. The electronic device (101) may recommend the first product (201) and the second product (202) (e.g., similar product, alternative product) at least partially similar to the first product (201) based on a user interface of a similar product recommendation program.

[0126] Referring to FIG. 8, the processor (120) of the electronic device (101) can perform a first processing process (801) (e.g., batch platform, BATCH PROCESS) for managing an object embedding model and a second processing process (802) (e.g., real-time platform, REAL-TIME PROCESS) for selecting similar products and providing them to a user.

[0127] The first processing step (801) can be divided into a first operation (810) (e.g., collecting metadata) and a second operation (820) (e.g., analyzing similar object properties), and by performing the first operation (810) and the second operation (820), an object embedding model can be managed. For example, the first operation (810) can include an operation of collecting metadata (e.g., category information (301) of FIG. 3, identification information (302) of FIG. 3) related to a product by a developer and an administrator. When metadata related to at least one product is input, the electronic device (101) can collect the metadata based on a similar product recommendation program. In operation 851, the processor (120) can perform a second operation (820) based on the collected metadata. For example, the second operation (820) can include an operation of matching and managing at least partially similar products using the collected metadata. The electronic device (101) can match a first product (e.g., the first product (201) of FIG. 2) and a second product (e.g., the second product (202) of FIG. 2) that is at least partially similar to the first product (201) based on an object embedding model. For example, when a product search request for the first product (201) is confirmed, the electronic device (101) can select a second product (202) that is at least partially matched based on the first product (201) and output the selected second product (202).

[0128] Referring to FIG. 8, the second operation (820) is illustrated as performing one of about four processing methods (821, 822, 823, 824), but is not limited thereto.

[0129] In the second operation (820), the first method (821) (metadata match) may be a method of managing the first product (201) and the second product (202) to be matched with each other in response to a situation in which metadata (e.g., object information (311) included in identification information (302)) between the first product (201) and the second product (202) match with each other. For example, the processor (120) may search for at least one second product (202) having the same category information based on the category information (301) of the first product (201), and select the second product (202) in which the identification information (302) between the first product (201) and the at least one second product (202) at least partially matches. The processor (120) can manage the first product (201) and the second product (202) to be matched with each other based on the object embedding model. For example, the processor (120) can check whether there is a match between the first code information for the first product (201) generated based on the object embedding model and the second code information for the second product (202) generated based on the object embedding model, and can select the second product (202) corresponding to the second code information that at least partially matches the first code information of the first product (201).

[0130] In the second operation (820), the second method (822) (matching metadata embedding vectors) may be a method of comparing and analyzing the first product (201) and the second product (202) in a situation where the metadata (e.g., identification information (302)) between the first product (201) and the second product (202) partially do not match (e.g., the category information (301) do not match each other), thereby determining whether the first product (201) and the second product (202) match each other. For example, if some objects included in the metadata are missing or an error occurs, the processor (120) may generate other objects to replace the some objects based on the object embedding model. The processor (120) can compare the first product (201) and the second product (202), and if the set matching condition is satisfied, manage the first product (201) and the second product (202) so that they are matched with each other.

[0131] In the second operation (820), the third method (823) (matching object embedding vectors) may be a method of comparing first identification information (e.g., first identification information (611) of FIG. 6) for the first product (201) with second identification information (e.g., second identification information (612) of FIG. 6) for the second product (202) in a situation where metadata (e.g., identification information (302)) between the first product (201) and the second product (202) do not at least partially match, thereby determining whether there is a match between the first product (201) and the second product (202). For example, the processor (120) may determine whether there is a match with the second identification information (612) based on object information included in the first identification information (611) and arrangement information for the object. The processor (120) can generate first code information (e.g., code information (521) of FIG. 5) by encoding the first identification information, and can generate second code information by encoding the second identification information. Based on the first code information of the first product (201) and the second code information of the second product (202), the processor (120) can manage the first product (201) and the second product (202) to be matched with each other. For example, the method of generating code information according to the third method (823) can be replaced with the description related to FIG. 5.

[0132] In the second operation (820), the fourth method (824) (heuristics) may be a method of comparing the first identification information (e.g., the first identification information (611) of FIG. 6) of the first product (201) with the second identification information (e.g., the second identification information (612) of FIG. 6) of the second product (202) in a situation where the metadata (e.g., the identification information (302)) between the first product (201) and the second product (202) do not match to determine whether the first product (201) and the second product (202) match. The first identification information (611) and the second identification information (612) may be determined based on at least one character and at least one number. For example, when comparing the first identification information (611) and the second identification information (612), the processor (120) may use a first matching condition (prefix match) (e.g., the first matching condition (641) of FIG. 6), a second matching condition (ANS match) (e.g., the second matching condition (642) of FIG. 6), and a third matching condition (CHAR match) (e.g., the third matching condition (643) of FIG. 6)) to determine whether the first product (201) and the second product (202) match each other. The processor (120) may use the first matching condition (641), the second matching condition (642), and the third matching condition (643) illustrated in FIG. 6 to manage the first product (201) and the second product (202) to match each other. For example, a method using a matching condition between the first product (201) and the second product (202) (e.g., the fourth method (824)) can be replaced with the description related to FIGS. 6 and 7.

[0133] According to one embodiment, the electronic device (101) can manage the object embedding model by periodically or aperiodically performing the first operation (810) and the second operation (820) included in the first processing process (801). The processor (120) can select at least one second product (202) matching the first product (201) based on the object embedding model, and can provide the selected second product (202) to the user.

[0134] The second processing step (802) can be divided into a third operation (830) (e.g., similar object search) and a fourth operation (840) (e.g., search results), and by performing the third operation (830) and the fourth operation (840), at least one second product (202) can be selected based on the object embedding model. The electronic device (101) can provide the selected at least one second product (202) to the user.

[0135] Referring to FIG. 8, the processor (120) of the electronic device (101) may perform a third operation (830) in response to a product search request. For example, the processor (120) may identify a first product (201) according to the product search request, and select a second product (202) that is at least partially similar to the first product (201) based on an object embedding model. In operation 852, the processor (120) may obtain information related to the second product (202) selected by the object embedding model. Based on the obtained information related to the second product (202), the processor (120) may determine at least one second product (202) that has a relatively high degree of similarity (e.g., high relevance). In operation 853, the processor (120) may return information about at least one determined second product (202) to the object embedding model and update the object embedding model. For example, the object embedding model may independently update information related to the first product (201) and information related to the second product (202) matched to the first product (201). The object embedding model may also change the priority of the second product (202) matched to the first product (201). In operation 854, the processor (120) may perform the fourth operation (840) based on the information about the at least one determined second product (202). For example, in response to a product search request for a first product (201), the processor (120) may determine at least one second product (202) and display the determined at least one second product (202) as a search result through a display (e.g., display (160) of FIG. 3). The processor (120) may provide the user with a second product (e.g., a similar product, a substitute product) that is at least partially similar to the first product (201).

[0136] According to one embodiment, the processor (120) may display a first product (201) and at least one second product (202) on a single screen based on a user interface (UI) based on a similar product recommendation program. The electronic device (101) may select at least one second product (202) having a relatively high degree of similarity with respect to the first product (201) and provide the selected at least one second product (202) to the user.

[0137] According to one embodiment, the electronic device (101) can select a second product (202) (e.g., a similar product, a recommended product) having a relatively high similarity to the first product (201) using an object embedding model, and can provide the selected second product to the user more efficiently.

[0138] In a method for recommending similar products of an electronic device according to one embodiment, the method may include, in response to a search request related to at least one product, an operation of confirming a first product (e.g., a first product (201) of FIG. 2), an operation of confirming identification information of a category related to the first product (201) (e.g., identification information (302) of FIG. 3) based on an object embedding model, an operation of confirming arrangement information of a plurality of objects included in the identification information (302), an operation of searching for at least one second product (202) related to the first product (201) based on the arrangement information of the plurality of objects, and an operation of outputting information related to the first product (201) and the searched at least one second product (202).

[0139] According to one embodiment, the operation of searching for at least one second product (202) may include an operation of calculating a similarity corresponding to the at least one second product (202) based on arrangement information of a plurality of objects, and an operation of searching for the at least one second product (202) based on the calculated similarity.

[0140] According to one embodiment, each object included in the identification information (302) may include at least one letter, at least one number, and at least one symbol.

[0141] A method for recommending similar products of an electronic device according to one embodiment may further include an operation of setting a weight corresponding to each object included in identification information (302), and an operation of applying the set weight corresponding to the matched code information in response to a situation in which first code information corresponding to the first product (201) and second code information corresponding to the second product (202) at least partially match each other.

[0142] According to one embodiment, each object constituting the plurality of objects may include information indicating at least one of product category information, product type information, product option information, product function information, product color information, product manufacturing date information, and product manufacturing country information related to the at least one product.

[0143] According to one embodiment, the operation of searching for at least one second product (202) may include, if the first object is not included among the plurality of objects, identifying a second object related to the first object based on the object embedding model, and searching for the at least one second product (202) that at least partially matches the identified second object based on the object embedding model.

[0144] According to one embodiment, the operation of searching for at least one second product (202) may include an operation of checking a weight set based on arrangement information for each of the objects, an operation of applying the set weight corresponding to each of the plurality of objects, and an operation of searching for the at least one second product (202) based on a similarity according to the set weight.

[0145] A method for recommending similar products of an electronic device according to one embodiment may further include an operation of updating the object embedding model based on the at least one second product (202), in response to information related to the at least one second product (202) being output.

[0146] A method for recommending similar products of an electronic device according to one embodiment may further include an operation of transmitting product information related to the first product (201) to an external electronic device (108) via a communication circuit (190), an operation of receiving at least one second product (202) related to the first product (201) searched based on an object embedding model of the external electronic device (108), and an operation of outputting information related to the received at least one second product (202).

[0147] In a non-transitory computer-readable storage medium storing one or more programs for executing a method for recommending similar products of an electronic device (101), the one or more programs, when executed by a processor (120) of the electronic device (101), may include an operation of identifying a first product (201) in response to a search request related to at least one product, an operation of identifying identification information (302) of a category related to the first product (201) based on an object embedding model, an operation of identifying arrangement information of a plurality of objects included in the identification information (302) within the identification information (302), an operation of searching for at least one second product (202) related to the first product (201) based on the arrangement information of the plurality of objects, and an operation of outputting information related to the first product (201) and the searched at least one second product (202).

[0148] According to one embodiment, the operation of searching for at least one second product (202) may include an operation of calculating a similarity corresponding to the at least one second product (202) based on arrangement information of the plurality of objects, and an operation of searching for the at least one second product (202) based on the calculated similarity.

[0149] Electronic devices according to the various embodiments disclosed in this document may take various forms. Electronic devices may include, for example, portable communication devices (e.g., smartphones), computer devices, portable multimedia devices, portable medical devices, cameras, wearable devices, or home appliances. Electronic devices according to the embodiments of this document are not limited to the aforementioned devices.

[0150] The various embodiments of this document and the terminology used therein are not intended to limit the technical features described in this document to specific embodiments, but should be understood to include various modifications, equivalents, or substitutes of the embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of the items, unless the context clearly indicates otherwise. In this document, each of the phrases "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" can include any one of the items listed together in the corresponding phrase among those phrases, or all possible combinations thereof. Terms such as "first," "second," or "first" or "second" may be used merely to distinguish one component from another, and do not limit the components in any other respect (e.g., importance or order). When a component (e.g., a first component) is referred to as "coupled" or "connected" to another component (e.g., a second component), with or without the terms "functionally" or "communicatively," it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or through a third component.

[0151] The term "module" used in various embodiments of this document may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit. A module may be an integral component, or a minimum unit or part of such a component that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0152] Various embodiments of the present document may be implemented as software (e.g., a program (140)) including one or more instructions stored in a storage medium (e.g., an internal memory (136) or an external memory (138)) readable by a machine (e.g., an electronic device (101)). For example, a processor (e.g., a processor (120)) of the machine (e.g., an electronic device (101)) may call at least one instruction among the one or more instructions stored from the storage medium and execute it. This enables the machine to operate to perform at least one function according to the at least one called instruction. The one or more instructions may include code generated by a compiler or code executable by an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' simply means that the storage medium is a tangible device and does not contain signals (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently or temporarily on the storage medium.

[0153] According to one embodiment, the method according to various embodiments disclosed in the present document may be provided as included in a computer program product. The computer program product may be traded as a product between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or may be distributed online (e.g., downloaded or uploaded) through an application store (e.g., Play Store™) or directly between two user devices (e.g., smart phones). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily generated in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0154] According to various embodiments, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and placed in other components. According to various embodiments, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to various embodiments, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

Claims

1. In an electronic device (101), display (160); memory (130); and It includes a processor (120) operatively connected to the display (160) and the memory (130), The above processor (120) In response to a search request involving at least one product, a first product (201) is identified, Based on the object embedding model, the identification information (302) of the category related to the first product (201) is verified, For the plurality of objects included in the above identification information (302), the arrangement information of the plurality of objects is confirmed within the above identification information (320), Based on the arrangement information of the above plurality of objects, at least one second product (202) related to the first product (201) is searched, An electronic device that outputs information related to at least one second product (202) searched through the display (160).

2. In paragraph 1, The above processor (120) Based on the arrangement information of the above plurality of objects, a similarity corresponding to at least one second product is calculated, An electronic device that searches for at least one second product based on the calculated similarity.

3. In paragraph 1, An electronic device wherein each object included in the above identification information is set to include at least one letter, at least one number, and at least one symbol.

4. In paragraph 1, The above processor (120) Set a weight corresponding to each object included in the above identification information, An electronic device that applies the set weight in response to a situation where the first code information corresponding to the first product and the second code information corresponding to the second product at least partially match each other.

5. In paragraph 1, An electronic device comprising a plurality of objects, each object including information representing at least one of product category information, product type information, product option information, product function information, product color information, product manufacturing date information, and product manufacturing country information related to at least one product.

6. In paragraph 1, The above processor (120) If the first object is not included among the above multiple objects, a second object related to the first object is identified based on the object embedding model, An electronic device that searches for at least one second product that at least partially matches the identified second object based on the object embedding model.

7. In paragraph 1, The above processor (120) For each of the above objects, check the weight set based on the array information, Applying the set weight to each of the above multiple objects, An electronic device that searches for at least one second product based on a similarity according to the above-described set weights.

8. In paragraph 1, The above processor (102) An electronic device that updates the object embedding model based on the at least one second product, in response to information relating to the at least one second product being output.

9. In paragraph 1, Further comprising a communication circuit (190) for communicating with an external electronic device (108); The above processor (120) Through the above communication circuit (190), product information related to the first product is transmitted to the external electronic device, Receive at least one second product related to the first product, which is retrieved based on an object embedding model of the external electronic device; An electronic device that outputs information related to at least one second product received above.

10. In paragraph 1, The above processor (120) An electronic device that identifies the first product (201) based on at least one of a description, text information, category information, and identification information corresponding to the at least one product according to the search request.

11. In a method for recommending similar products of electronic devices (101), In response to a search request involving at least one product, the act of identifying a first product (201); An operation of verifying identification information (302) of a category related to the first product (201) based on an object embedding model; An operation of confirming arrangement information of a plurality of objects included in the above identification information (302) within the above identification information (302); An operation of searching for at least one second product (202) related to the first product (201) based on the arrangement information of the plurality of objects; and A method comprising: an operation of outputting information related to the first product (201) and at least one second product (202) searched for; 12. In paragraph 11, The operation of searching for at least one second product (202) comprises: An operation of calculating a similarity corresponding to at least one second product (202) based on the arrangement information of the plurality of objects; and A method comprising: an operation of searching for at least one second product (202) based on the calculated similarity; 13. In paragraph 11, An operation of setting a weight corresponding to each object included in the above identification information (302); and A method further comprising: an operation of applying the set weight corresponding to the matched code information in response to a situation where the first code information corresponding to the first product (201) and the second code information corresponding to the second product (202) at least partially match each other; 14. In paragraph 11, The operation of searching for at least one second product (202) comprises: If the first object is not included among the plurality of objects, an operation of identifying a second object related to the first object based on the object embedding model; and A method comprising: an operation of searching for at least one second product (202) that at least partially matches the identified second object based on the object embedding model; 15. In a non-transitory computer-readable storage medium storing one or more programs for executing a method for recommending similar products of an electronic device (101), The above one or more programs, when executed by the processor (120) of the electronic device (101), In response to a search request involving at least one product, the act of identifying a first product (201); An operation of verifying identification information (302) of a category related to the first product (201) based on an object embedding model; An operation of confirming arrangement information of a plurality of objects included in the above identification information (302) within the above identification information (302); An operation of searching for at least one second product (202) related to the first product (201) based on the arrangement information of the plurality of objects; and A computer-readable storage medium comprising: an operation of outputting information related to the first product (201) and at least one second product (202) searched for;

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