KR102615815B1 - Method and system for providing special sales events based on user's intent of search queries - Google Patents

Abstract

This disclosure relates to a method for generating a product promotion based on user intent of a search query. The method of generating a product promotion includes receiving data associated with a plurality of product inquiries, extracting a trend associated with at least one of a specific group or a specific period based on the data associated with the plurality of product inquiries, and extracting a trend associated with the extracted trend. It includes the step of creating a product exhibition including planned products.

Description

Method and system for creating product promotions based on user intent of search queries {METHOD AND SYSTEM FOR PROVIDING SPECIAL SALES EVENTS BASED ON USER'S INTENT OF SEARCH QUERIES}

The present disclosure relates to a method and system for generating a special product exhibition based on user intent of a search query. Specifically, the method and system for extracting a trend based on data associated with a plurality of product queries and generating a special exhibition including special products related to the trend. It relates to methods and systems.

Recently, various online product information provision methods and product sales methods have been developed to help consumers' shopping convenience, such as television home shopping broadcasts, Internet shopping, and catalog shopping. Product sellers, shopping mall operators, open market operators, etc. can plan product promotions to enable products to be purchased cheaper or on better terms as a means of promoting or marketing online shopping services. For example, a product seller can target specific consumers, collect products expected to be needed by the target consumers, and provide an event where the products can be purchased at a discounted price. Alternatively, a product seller, etc. can collect products expected to be needed by consumers at a specific time and provide information about the products to consumers at that time (or earlier than that time). Through these product promotions, consumers at a specific time or belonging to a specific group can check and purchase the products they need at a glance. In addition, product sellers, shopping mall operators, open market operators, etc. can improve sales by changing the status of stagnant product sales through product promotions and entice potential customers to use related online shopping services.

According to conventional technology, product sellers, shopping mall operators, open market operators, etc. must passively analyze and understand consumers' shopping trends and create special exhibitions corresponding to the shopping trends. In other words, operators who want to create a special exhibition must directly identify shopping trends and prepare a theme or title for the special exhibition that fits the trend. In addition, there is an inconvenience in that operators who want to create a special exhibition must manually search for or enter products that match the title and/or theme of the special exhibition, that is, products to be included as special exhibitions.

The present disclosure provides a method for creating a product promotion, a computer program stored in a recording medium, and a device (system) to solve the above problems.

The present disclosure may be implemented in various ways, including as a method, device (system), or computer program stored in a readable storage medium.

According to an embodiment of the present disclosure, a method of generating a product promotion based on user intent of a search query performed by at least one processor includes receiving data associated with a plurality of product queries, Based on this, it includes extracting a trend associated with at least one of a specific group or a specific period, and generating a product promotion including special products related to the extracted trend.

According to another embodiment of the present disclosure, a method of learning a special product extraction model performed by at least one computing device includes receiving data about a learning special product, receiving information about each of a plurality of products, and Based on data about the learning special exhibition and information about each of the plurality of products, as the title of the special exhibition is input, it includes learning a special product extraction model to output one or more products related to the title of the special exhibition.

According to another embodiment of the present disclosure, a computer program stored in a computer-readable recording medium is provided to execute the above-described method on a computer.

According to another embodiment of the present disclosure, the information processing system includes a communication module, a memory, and at least one processor connected to the memory and configured to execute at least one computer-readable program included in the memory, and at least one The program receives data associated with a plurality of product queries, extracts trends associated with at least one of a specific group or a specific period based on the data associated with the plurality of product queries, and includes planned products associated with the extracted trends. Contains commands for creating product promotions.

According to another embodiment of the present disclosure, the information processing system includes a communication module, a memory, and at least one processor connected to the memory and configured to execute at least one computer-readable program included in the memory, and at least one The program receives data about the learning special exhibition, receives information about each of a plurality of products, and based on the data about the learning special exhibition and information about each of the plurality of products, as the title of the special exhibition is input, the special exhibition Includes commands for learning a product extraction model to output one or more products related to the title.

In various embodiments of the present disclosure, product queries searched by consumers with similar characteristics and/or product queries searched by consumers at similar times are determined to be related to the same shopping purpose or intention, and the shopping purpose or intention is the same. Product queries or keywords can be clustered into multiple clusters. By extracting shopping trends from a plurality of clustered clusters, it is possible to identify shopping trends that have not been discovered by users (e.g., operators of special exhibitions) and, accordingly, to create more diverse special exhibitions.

In various embodiments of the present disclosure, by entering a keyword for a shopping trend (e.g., a product query corresponding to a shopping trend, a keyword indicating the subject of the special exhibition, target consumer, time, etc.), the title of the special exhibition is automatically changed. can be created.

In various embodiments of the present disclosure, special products to be included in a special exhibition may be automatically determined. Accordingly, the operational resources required for a user (eg, the operator of a special exhibition) to search for and find products to be included as special products in the special exhibition can be reduced. In addition, using the semantic similarity between the product characteristics extracted from information such as the product title, category, introduction, review, tag, image, etc., and the title of the special exhibition, products matching the theme of the special exhibition to be created are automatically extracted. It can be. Accordingly, the diversity and suitability of special products included in special exhibitions can be improved.

Embodiments of the present disclosure will be described with reference to the accompanying drawings described below, in which like reference numerals indicate like elements, but are not limited thereto.
1 is a diagram illustrating an example in which a user is provided with shopping content through a screen of a user terminal according to an embodiment of the present disclosure.
Figure 2 is a schematic diagram showing a configuration in which an information processing system according to an embodiment of the present disclosure is connected to enable communication with a plurality of user terminals.
Figure 3 is a block diagram showing the internal configuration of the user terminal 210 and the information processing system according to an embodiment of the present disclosure.
Figure 4 is a flowchart showing a product promotion creation method according to an embodiment of the present disclosure.
Figure 5 is a diagram illustrating an example of extracting trend keywords from data associated with a plurality of product inquiries according to an embodiment of the present disclosure.
FIG. 6 is a diagram illustrating an example of determining a title of a special exhibition representing the theme of a trend based on a trend keyword according to an embodiment of the present disclosure.
Figure 7 is a diagram illustrating an example of extracting one or more products related to a trend from a plurality of products based on the title of a special exhibition according to an embodiment of the present disclosure.
Figure 8 is a diagram showing an example of learning a special product extraction model according to an embodiment of the present disclosure.
Figure 9 is a diagram illustrating an example in which a user (eg, an operator) creates a product promotion according to an embodiment of the present disclosure.
Figure 10 is a diagram illustrating an example in which a user (eg, an operator) is provided with one or more product promotion candidates according to an embodiment of the present disclosure.

Hereinafter, specific details for implementing the present disclosure will be described in detail with reference to the attached drawings. However, in the following description, detailed descriptions of well-known functions or configurations will be omitted if there is a risk of unnecessarily obscuring the gist of the present disclosure.

In the accompanying drawings, identical or corresponding components are given the same reference numerals. Additionally, in the description of the following embodiments, overlapping descriptions of identical or corresponding components may be omitted. However, even if descriptions of components are omitted, it is not intended that such components are not included in any embodiment.

Advantages and features of the disclosed embodiments and methods for achieving them will become clear by referring to the embodiments described below in conjunction with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below and may be implemented in various different forms. The present embodiments are merely intended to ensure that the present disclosure is complete and that the present disclosure does not convey the scope of the invention to those skilled in the art. It is provided only for complete information.

Terms used in this specification will be briefly described, and the disclosed embodiments will be described in detail. The terms used in this specification are general terms that are currently widely used as much as possible while considering the function in the present disclosure, but this may vary depending on the intention or precedent of a technician working in the related field, the emergence of new technology, etc. In addition, in certain cases, there are terms arbitrarily selected by the applicant, and in this case, the meaning will be described in detail in the description of the relevant invention. Accordingly, the terms used in this disclosure should be defined based on the meaning of the term and the overall content of the present disclosure, rather than simply the name of the term.

In this specification, singular expressions include plural expressions, unless the context clearly specifies the singular. Additionally, plural expressions include singular expressions, unless the context clearly specifies plural expressions. When it is said that a certain part includes a certain element throughout the specification, this does not mean excluding other elements, but may further include other elements, unless specifically stated to the contrary.

Additionally, the term 'module' or 'unit' used in the specification refers to a software or hardware component, and the 'module' or 'unit' performs certain roles. However, 'module' or 'unit' is not limited to software or hardware. A 'module' or 'unit' may be configured to reside on an addressable storage medium and may be configured to run on one or more processors. Thus, as an example, a 'module' or 'part' refers to components such as software components, object-oriented software components, class components and task components, processes, functions and properties. , procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, or variables. Components and 'modules' or 'parts' may be combined into smaller components and 'modules' or 'parts' or further components and 'modules' or 'parts'. Could be further separated.

According to an embodiment of the present disclosure, a 'module' or 'unit' may be implemented with a processor and memory. 'Processor' should be interpreted broadly to include general-purpose processors, central processing units (CPUs), microprocessors, digital signal processors (DSPs), controllers, microcontrollers, state machines, etc. In some contexts, 'processor' may refer to an application-specific integrated circuit (ASIC), programmable logic device (PLD), field programmable gate array (FPGA), etc. 'Processor' refers to a combination of processing devices, for example, a combination of a DSP and a microprocessor, a combination of a plurality of microprocessors, a combination of one or more microprocessors in combination with a DSP core, or any other such combination of configurations. You may. Additionally, 'memory' should be interpreted broadly to include any electronic component capable of storing electronic information. 'Memory' refers to random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable-programmable read-only memory (EPROM), May also refer to various types of processor-readable media, such as electrically erasable PROM (EEPROM), flash memory, magnetic or optical data storage, registers, etc. A memory is said to be in electronic communication with a processor if the processor can read information from and/or write information to the memory. The memory integrated into the processor is in electronic communication with the processor.

In this disclosure, 'shopping content' refers to online shopping services connected to the Internet, broadcasts about shopping products provided through social media, information on product promotions, shopping product information, shopping payment information, shopping seller or manufacturer information, etc. It can refer to information or content related to online shopping. For example, shopping content may include, but is not limited to, content containing text, images or videos for descriptions, advertisements, and promotions of products subject to online shopping, live broadcast content, recorded broadcast content, etc. . As another example, shopping content may include the title of the product promotion, tags related to the product promotion, information about the products in the product promotion, etc.

In the present disclosure, 'special sales events' may generally refer to an event that advertises, displays, or sells one or more products related to a specific theme during a specific period. For example, a special exhibition may be provided through an online shopping service, social media, etc., and may be provided to a specific group of users during a specific time (e.g., a season, a specific month of the year, a specific date or time). In another example, a special exhibition may be created or provided to advertise, display, or sell one or more products related to a specific product topic, such as summer vacation clothing, children's toys, camping supplies, etc. In one embodiment, the special exhibition includes the theme of the special exhibition, the title of the special exhibition indicating the product to be sold, etc., special product information (e.g., product image, product price, product description, etc.), the validity period and/or sales conditions of the special exhibition. It may be expressed as data including, but is not limited to this.

FIG. 1 is a diagram illustrating an example in which a user 100 is provided with shopping content through the screen 120 of the user terminal 110 according to an embodiment of the present disclosure. The screen 120 shown in FIG. 1 is displayed when a user (eg, consumer) 100 executes an application that provides shopping content on the user terminal (eg, smartphone) 110 to display shopping content. This shows an example of receiving information about a product promotion. For example, as shown, the user 100 uses the user terminal 110 as shopping content to include the title of a specific product promotion ('How about decorating your home with lighting?') and a hashtag ('# 'Table lamp', '#Floor lamp', '#Furniture', '#Lighting') and information on special products included in the relevant product promotion (e.g., product image, product name, product price, etc.) there is.

In order to provide a product promotion to the user 100, an information processing system that communicates with the user terminal 110 may create one or more product promotions and transmit them to the user terminal 110. In one embodiment, the information processing system may automatically generate one or more product promotions based on various search queries (eg, product search queries). In this case, the information processing system provides automatically generated information about one or more product promotions to the operator of the product promotion, and in response to the operator's selection of at least one of the one or more product promotions, issues the selected product promotion. You can. Alternatively, the information processing system may immediately publish one or more automatically generated product promotions. In another embodiment, the information processing system generates a topic candidate for a special exhibition, a target consumer candidate for a special exhibition, a trend keyword candidate for a special exhibition, a title candidate for a special exhibition, and/or a candidate for a planned product to be included in the special exhibition, and provides them to the operator of the product exhibition. You can. In this case, in response to the operator's selection of at least one of the subject candidate for the special exhibition, the candidate for the target consumer of the special exhibition, the trend keyword candidate for the special exhibition, the title candidate for the special exhibition, and/or the candidate for the planned product to be included in the special exhibition, the information processing system operates one or more You can create a product promotion.

The information processing system may receive data associated with a plurality of product inquiries to generate a product promotion. Here, 'product inquiry' may refer to a search query entered by a user through an online shopping application or web page, search application or web page run by the user terminal, for the purpose of searching or purchasing a specific product. . In addition, data related to a product inquiry includes the product query itself (e.g., search query word, text representing the search keyword, image, etc.), the date and time the product inquiry was entered, and information on the user terminal where the product inquiry was entered. (eg, location of the user terminal, IP information, etc.), and information of the user who entered the product inquiry (eg, user account or profile information, user age, gender, occupation, etc.).

The information processing system may extract a trend associated with at least one of a specific group or a specific period based on data associated with a plurality of product queries, and create a special exhibition including special products related to the extracted trend. Here, a specific group may refer to a group created by classifying a plurality of users based on one or more criteria (eg, age, gender, interests, occupation, etc.). For example, specific groups include groups classified by age such as teenagers, 20s, and 30s, groups classified by occupation such as office workers, self-employed, and college students, groups classified by gender such as men and women, married people, and It may include groups classified according to married status, such as single people. Additionally, a specific period may include a season, an anniversary, a weekday or weekend, a public holiday, a holiday period, a specific year, a specific month of the year, a specific week of the month, a section between specific points in the day, etc.

The information processing system clusters data associated with a plurality of product inquiries received into a plurality of clusters, and extracts one or more trend keywords associated with at least one of a specific group or a specific period from one or more of the plurality of clusters. You can. For example, the information processing system divides data associated with a plurality of product queries into a plurality of clusters based on the query intention, topic, information of the user who searched for the product query, the date and time the product query was entered, etc. It can be clustered. For example, the information processing system may cluster data associated with multiple product queries into multiple clusters using a clustering model based on statistics or probability, such as a statistical theme embedding model.

Then, the information processing system may determine a title for the special exhibition that represents the topic of the trend based on one or more trend keywords. For example, the information processing system may combine one or more trend keywords to generate a title for a special exhibition composed of a phrase or sentence representing the topic of the trend. Alternatively or additionally, the information processing system may use a language model, such as a language model based on statistics or a language model based on an artificial neural network, to convert (or generate) one or more trend keywords into a title of a special exhibition that represents the topic of the trend. can do.

Then, the information processing system determines one or more products associated with one or more trend keywords among the plurality of products as a planned product. You can create a special exhibition that includes special products. In one embodiment, the information processing system may determine one or more products associated with one or more trend keywords among a plurality of products as the special product, based on the title of the special exhibition. For example, the information processing system uses a special product extraction model to extract one or more products associated with one or more trend keywords that match the title of the special exhibition from among a plurality of products, and selects the extracted one or more products as special products. You can decide. Additionally or alternatively, the information processing system extracts at least one product that matches the title of the special exhibition from among the plurality of products using a special product extraction model, and sends a list containing the extracted at least one product to the user terminal. It can be printed through . Thereafter, the information processing system may determine one or more products as the special product in response to the user's selection of one or more products from the output list.

In one embodiment, the special product extraction model collects information about each of a plurality of products (e.g., product name, product description, category, related product, tag, introduction, review, image, price, brand, manufacturer, etc.) As a basis, a matching score with the title of the special exhibition for each of the plurality of products is calculated, and based on the calculated matching score, a predetermined number of products are extracted from the plurality of products, and the extracted predetermined number of products are included. It may correspond to a model learned to output a list of products. Additionally or alternatively, the special product extraction model may include an embedding generation model learned to generate embedding data for each of the plurality of products and embedding data for the title of the special exhibition. In this case, the special product extraction model calculates a matching score for each of the plurality of products with the title of the special exhibition based on the embedding data for each of the plurality of products and the embedding data for the title of the special exhibition, and the calculated matching score Based on this, it may correspond to a model learned to extract a predetermined number of products from a plurality of products and output a product list including the extracted predetermined number of products.

According to the embodiments described above, trends related to a specific group and/or a specific period can be extracted by reflecting information on a plurality of product inquiries actually entered by consumers, and special exhibitions tailored to the extracted trends can be created. there is. Accordingly, the operator of the product plan can create a product plan by identifying trends that he or she had not yet considered in a timely manner, and the product plan can include various planned products that fit the newly identified trend. In addition, as a product exhibition having a special exhibition theme, title of special exhibition, and/or special product that matches the extracted trend is automatically created, the operator's work burden can be reduced. In addition, as special exhibition themes, exhibition titles, and/or special products that match current product sales trends are automatically recommended, operators can efficiently create/publish special exhibitions.

Figure 2 is a schematic diagram showing a configuration in which the information processing system 230 according to an embodiment of the present disclosure is connected to communicate with a plurality of user terminals 210_1, 210_2, and 210_3. As shown, a plurality of user terminals 210_1, 210_2, and 210_3 may be connected to the information processing system 230 that can provide a product promotion service and/or a product promotion creation service through the network 220. Here, the plurality of user terminals 210_1, 210_2, and 210_3 may include terminals of consumers who will be provided with the product promotion and/or operators (for example, product promotion planners or operators, etc.) who will be provided with the product promotion creation service. . In one embodiment, the information processing system 230 is one or more capable of storing, providing, and executing computer-executable programs (e.g., downloadable applications) and data related to product promotion provision services and/or product promotion creation services, etc. It may include a server device and/or a database, or one or more distributed computing devices and/or a distributed database based on a cloud computing service.

The product exhibition provision service and/or product exhibition creation service provided by the information processing system 230 includes a product exhibition creation application, a shopping application, a search application, and a mobile product exhibition creation application installed on each of the plurality of user terminals 210_1, 210_2, and 210_3. It may be provided to the user through a browser application or web browser. For example, the information processing system 230 may provide information or perform processing corresponding to a request for product promotion creation received from the user terminals 210_1, 210_2, and 210_3 through a product promotion creation application, etc. there is.

A plurality of user terminals 210_1, 210_2, and 210_3 may communicate with the information processing system 230 through the network 220. The network 220 may be configured to enable communication between a plurality of user terminals 210_1, 210_2, and 210_3 and the information processing system 230. Depending on the installation environment, the network 220 may be, for example, a wired network such as Ethernet, a wired home network (Power Line Communication), a telephone line communication device, and RS-serial communication, a mobile communication network, a wireless LAN (WLAN), It may consist of wireless networks such as Wi-Fi, Bluetooth, and ZigBee, or a combination thereof. The communication method is not limited, and may include communication methods utilizing communication networks that the network 220 may include (e.g., mobile communication networks, wired Internet, wireless Internet, broadcasting networks, satellite networks, etc.) as well as user terminals (210_1, 210_2, 210_3). ) may also include short-range wireless communication between

In Figure 2, the mobile phone terminal (210_1), tablet terminal (210_2), and PC terminal (210_3) are shown as examples of user terminals, but they are not limited thereto, and the user terminals (210_1, 210_2, 210_3) use wired and/or wireless communication. This is possible and may be any computing device on which a product promotion creation application, shopping application, search application, mobile browser application, or web browser, etc. can be installed and executed. For example, user terminals include AI speakers, smartphones, mobile phones, navigation, computers, laptops, digital broadcasting terminals, PDAs (Personal Digital Assistants), PMPs (Portable Multimedia Players), tablet PCs, game consoles, It may include wearable devices, IoT (internet of things) devices, VR (virtual reality) devices, AR (augmented reality) devices, set-top boxes, etc. In addition, in Figure 2, three user terminals (210_1, 210_2, 210_3) are shown as communicating with the information processing system 230 through the network 220, but this is not limited to this, and a different number of user terminals are connected to the network ( It may be configured to communicate with the information processing system 230 through 220).

According to one embodiment, the information processing system 230 may receive data associated with a plurality of product inquiries from a plurality of user terminals 210_1, 210_2, and 210_3. Then, the information processing system 230 generates a product promotion based on the data associated with the plurality of product inquiries thus received, and sends a product promotion to a plurality of user terminals 210_1, 210_2, and 210_3 (e.g., a product promotion operator and / or may be provided to the consumer's user terminal). Additionally or alternatively, the information processing system 230 may, based on data associated with a plurality of product queries, be a subject candidate for the special exhibition, a candidate for the special exhibition target consumer, a candidate for a trend keyword for the special exhibition, a candidate for the title of the special exhibition, and/or a candidate for the special exhibition. Candidates for planned products to be included may be generated and provided to a plurality of user terminals 210_1, 210_2, and 210_3 (eg, user terminals of product promotion operators). In this case, the information processing system 230 selects the user for at least one of the subject candidates of the special exhibition, the target consumer candidate of the special exhibition, the candidate of the trend keyword of the special exhibition, the title candidate of the special exhibition, and/or the special product candidate to be included in the special exhibition. By receiving information from the user terminals 210_1, 210_2, and 210_3, one or more product promotions can be created.

Figure 3 is a block diagram showing the internal configuration of the user terminal 210 and the information processing system 230 according to an embodiment of the present disclosure. The user terminal 210 may refer to any computing device capable of executing a product promotion creation application, a shopping application, a search application, a mobile browser application, or a web browser, and capable of wired/wireless communication, for example, Figure 2 It may include a mobile phone terminal (210_1), a tablet terminal (210_2), a PC terminal (210_3), etc. As shown, the user terminal 210 may include a memory 312, a processor 314, a communication module 316, and an input/output interface 318. Similarly, information processing system 230 may include memory 332, processor 334, communication module 336, and input/output interface 338. As shown in FIG. 3, the user terminal 210 and the information processing system 230 are configured to communicate information and/or data through the network 220 using respective communication modules 316 and 336. It can be. Additionally, the input/output device 320 may be configured to input information and/or data to the user terminal 210 through the input/output interface 318 or to output information and/or data generated from the user terminal 210.

Memories 312 and 332 may include any non-transitory computer-readable recording medium. According to one embodiment, the memories 312 and 332 are non-perishable mass storage devices such as random access memory (RAM), read only memory (ROM), disk drive, solid state drive (SSD), and flash memory. It may include a (permanent mass storage device). As another example, non-perishable mass storage devices such as ROM, SSD, flash memory, disk drive, etc. may be included in the user terminal 210 or the information processing system 230 as a separate persistent storage device that is distinct from memory. Additionally, the memories 312 and 332 may store an operating system and at least one program code (for example, code for a product promotion creation application installed and running on the user terminal 210).

These software components may be loaded from a computer-readable recording medium separate from the memories 312 and 332. This separate computer-readable recording medium may include a recording medium directly connectable to the user terminal 210 and the information processing system 230, for example, a floppy drive, disk, tape, DVD/CD- It may include computer-readable recording media such as ROM drives and memory cards. As another example, software components may be loaded into the memories 312 and 332 through a communication module rather than a computer-readable recording medium. For example, at least one program is loaded into memory 312, 332 based on a computer program installed by files provided over the network 220 by developers or a file distribution system that distributes installation files for applications. It can be.

The processors 314 and 334 may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input/output operations. Instructions may be provided to the processors 314 and 334 by memories 312 and 332 or communication modules 316 and 336. For example, the processors 314 and 334 may be configured to execute instructions received according to program codes stored in a recording device such as the memory 312 and 332.

The communication modules 316 and 336 may provide a configuration or function for the user terminal 210 and the information processing system 230 to communicate with each other through the network 220, and may provide a configuration or function for the user terminal 210 and/or information processing. The system 230 may provide a configuration or function for communicating with other user terminals or other systems (for example, a separate cloud system, etc.). For example, a request or data generated by the processor 314 of the user terminal 210 according to a program code stored in a recording device such as the memory 312 (e.g., a request for creating a product promotion, a request for issuing a product promotion) requests, data associated with multiple product inquiries, etc.) may be transmitted to the information processing system 230 through the network 220 under the control of the communication module 316. Conversely, a control signal or command provided under the control of the processor 334 of the information processing system 230 is transmitted through the communication module 316 of the user terminal 210 through the communication module 336 and the network 220. It may be received by the user terminal 210. For example, the user terminal 210 receives a topic candidate for a special exhibition, a candidate for a target consumer for a special exhibition, a candidate for a trend keyword for a special exhibition, a candidate for a title for a special exhibition, and/or a candidate for a special exhibition for a special exhibition through the communication module 316 from the information processing system 230. Candidates for planned products to be included can be received.

The input/output interface 318 may be a means for interfacing with the input/output device 320. As an example, input devices may include devices such as cameras, keyboards, microphones, mice, etc., including audio sensors and/or image sensors, and output devices may include devices such as displays, speakers, haptic feedback devices, etc. You can. As another example, the input/output interface 318 may be a means for interfacing with a device that has components or functions for performing input and output, such as a touch screen, integrated into one. For example, the processor 314 of the user terminal 210 uses information and/or data provided by the information processing system 230 or another user terminal when processing instructions of a computer program loaded in the memory 312. A service screen, etc. constructed by doing so may be displayed on the display through the input/output interface 318. In FIG. 3 , the input/output device 320 is shown not to be included in the user terminal 210, but the present invention is not limited to this and may be configured as a single device with the user terminal 210. Additionally, the input/output interface 338 of the information processing system 230 may be connected to the information processing system 230 or means for interfacing with a device (not shown) for input or output that the information processing system 230 may include. It can be. In FIG. 3, the input/output interfaces 318 and 338 are shown as elements configured separately from the processors 314 and 334, but the present invention is not limited thereto, and the input/output interfaces 318 and 338 may be configured to be included in the processors 314 and 334. there is.

The user terminal 210 and information processing system 230 may include more components than those in FIG. 3 . However, there is no need to clearly show most prior art components. According to one embodiment, the user terminal 210 may be implemented to include at least some of the input/output devices 320 described above. Additionally, the user terminal 210 may further include other components such as a transceiver, a global positioning system (GPS) module, a camera, various sensors, and a database. For example, if the user terminal 210 is a smartphone, it may include components generally included in a smartphone, such as an acceleration sensor, a gyro sensor, a camera module, various physical buttons, and a touch screen. Various components such as buttons using a panel, input/output ports, and a vibrator for vibration may be implemented to be further included in the user terminal 210. According to one embodiment, the processor 314 of the user terminal 210 may be configured to operate an application that provides a product promotion creation service. At this time, code associated with the corresponding application and/or program may be loaded into the memory 312 of the user terminal 210.

While a program for an application providing a product planning creation service is running, the processor 314 may use input devices such as a touch screen, keyboard, camera including an audio sensor and/or an image sensor, and a microphone connected to the input/output interface 318. You can receive input or selected text, images, videos, voices, and/or actions, and store the received text, images, videos, voices, and/or actions in the memory 312 or use the communication module 316. And it can be provided to the information processing system 230 through the network 220. For example, the processor 314 receives input indicating the user's selection of a special exhibition topic candidate, special exhibition target consumer candidate, special exhibition trend keyword candidate, special exhibition title candidate, and/or special product candidate to be included in the special exhibition. It can be provided to the information processing system 230 through the communication module 316 and the network 220. As another example, the processor 314 may receive a user input requesting the issuance of a created special exhibition and provide it to the information processing system 230 through the communication module 316 and the network 220. As another example, processor 314 receives user input entering a product query through input device 320, and transmits the product query and data associated with the product query to information through network 220 and communication module 316. It can be provided to the processing system 230.

The processor 314 of the user terminal 210 manages, processes, and/or stores information and/or data received from the input device 320, other user terminals, the information processing system 230, and/or a plurality of external systems. It can be configured to do so. Information and/or data processed by processor 314 may be provided to information processing system 230 via communication module 316 and network 220. The processor 314 of the user terminal 210 may transmit information and/or data to the input/output device 320 through the input/output interface 318 and output the information. For example, the processor 314 may display the received information and/or data on the screen of the user terminal.

The processor 334 of the information processing system 230 may be configured to manage, process, and/or store information and/or data received from a plurality of user terminals 210 and/or a plurality of external systems. Information and/or data processed by the processor 334 may be provided to the user terminal 210 through the communication module 336 and the network 220. In one embodiment, the processor 334 of the information processing system 230 generates a trend associated with at least one of a specific group or a specific period based on data associated with a plurality of product inquiries received from a plurality of user terminals 210. You can extract and create a special exhibition that includes special products related to the extracted trend.

The processor 334 of the information processing system 230 uses the output device 320, such as a display output capable device (e.g., touch screen, display, etc.), an audio output capable device (e.g., speaker), of the user terminal 210. It may be configured to output processed information and/or data. For example, the processor 334 of the information processing system 230 sends the created (or issued) product promotion (or information about the product promotion) to the user terminal 210 through the communication module 336 and the network 220. ), and may be configured to output the product promotion through a display output capable device of the user terminal 210, etc. As another example, the processor 334 of the information processing system 230 sends the user terminal 210 through the communication module 336 and the network 220, a subject candidate for the special exhibition, a target consumer candidate for the special exhibition, a trend keyword candidate for the special exhibition, It may be configured to provide a title candidate for a special exhibition and/or a candidate for a special product to be included in the special exhibition, and output them through a display output capable device of the user terminal 210, etc.

Figure 4 is a flowchart showing a product promotion creation method 400 according to an embodiment of the present disclosure. In one embodiment, the product promotion creation method 400 may be performed by a processor (eg, at least one processor of an information processing system). The product promotion creation method 400 may be initiated by the processor receiving data associated with a plurality of product queries (S410). For example, the processor may receive data associated with a plurality of product inquiries from a plurality of user terminals. Additionally or alternatively, the processor may receive data associated with a plurality of product queries from internal and/or external storage devices (e.g., a product query database, etc.). Here, the data associated with the product query includes the product query itself (e.g., search query, text representing the search keyword, image, etc.), the date and time the product query was entered, and information on the user terminal where the product query was entered ( For example, the location of the user terminal, IP information, etc.), and information of the user who entered the product inquiry (for example, user account or profile information, user age, gender, occupation, etc.).

The processor may extract a trend associated with at least one of a specific group or a specific period based on data associated with a plurality of product inquiries (S420). In one embodiment, the processor clusters data associated with a plurality of product queries into a plurality of clusters, and from one or more of the plurality of clusters, one or more trend keywords associated with at least one of a specific group or a specific time period (e.g., Search queries, topics, dates, etc.) can be extracted. For example, the processor may use a statistical topic embedding model to cluster data associated with multiple product queries into multiple clusters.

Then, the processor may create a product promotion including special products related to the extracted trend (S430). For example, the processor may determine one or more products related to one or more trend keywords among a plurality of products as special products and create a special product exhibition including the special products. In one embodiment, the processor determines the title of a special exhibition representing the subject of a trend based on one or more trend keywords, and, based on the title of the special exhibition, selects one or more products associated with one or more trend keywords from among a plurality of products as the special exhibition product. It can be decided as To this end, the processor may use a language model to convert one or more trend keywords into a title of a special exhibition that represents the topic of the trend.

For example, the processor may use a special product extraction model to extract one or more products associated with one or more trend keywords that match the title of the special exhibition from among a plurality of products, and determine one or more extracted products as special products. . Additionally or alternatively, the processor extracts at least one product that matches the title of the special exhibition from among the plurality of products using a special product extraction model, and sends a list containing the extracted at least one product through the user terminal. Can be printed. In this case, the processor may determine one or more products as a special product in response to the user's selection of one or more products from the output list.

In one embodiment, the special product extraction model calculates a matching score with the title of the special exhibition for each of the plurality of products based on information about each of the plurality of products, and selects at least one product based on the calculated matching score. It may correspond to a model learned to extract and output the corresponding product list. Furthermore, the planned product extraction model may correspond to a model learned to extract a predetermined number of products from a plurality of products and output a product list including the extracted predetermined number of products. Additionally or alternatively, the special product extraction model may include an embedding generation model learned to generate embedding data for each of the plurality of products and embedding data for the title of the special exhibition. In this case, the special product extraction model calculates a matching score for each of the plurality of products with the title of the special exhibition based on the embedding data for each of the plurality of products and the embedding data for the title of the special exhibition, and the calculated matching score Based on this, it may correspond to a model learned to extract a predetermined number of products from a plurality of products and output a product list including the extracted predetermined number of products.

Additionally or alternatively, the product extraction model is such that the matching score between the embedding data for the title of the learning project and the embedding data for the products included in the learning project is a match score between the embedding data for the title of the learning project and the embedding data for the products included in the learning project. It may correspond to a model learned using a triplet loss function that forces the matching score to be greater than the matching score between embedding data for products that do not exist. Here, the learning special exhibition may refer to a previously actually held special exhibition and/or a special exhibition artificially created to learn a special product extraction model.

FIG. 5 is a diagram illustrating an example of extracting a trend keyword 540 from data 510 associated with a plurality of product inquiries according to an embodiment of the present disclosure. Groups of users, including users with similar characteristics (e.g., age, occupation, interests, etc.), are grouped together at similar times (e.g., within a week, within a month, during certain seasons such as spring, or during certain periods such as holidays or anniversaries). Product queries searched for (period, etc.) may include the same or similar user intent (e.g., shopping intent, search intent) shared among the corresponding users. Product queries entered or searched according to the same or similar user intent may be clustered into one or more clusters, and users' product search or product shopping trends may be extracted from the clusters containing the same or similar user intent.

For example, a processor (e.g., at least one processor of an information processing system) generates one or more clusters containing product queries with the same or similar user intent (e.g., search intent, shopping intent, etc.); , trends of a specific group and/or a specific period can be extracted from one or more clusters created. Here, the specific group may refer to the group of users themselves who entered or searched product queries included in one or more groups. Additionally or alternatively, a specific group may refer to the gender, age, occupation, etc. of a user who entered or searched multiple product queries included in one or more groups. Additionally, product queries may include meaningful queries (eg, refined, filtered, and/or preprocessed shopping queries) among queries entered or searched by users for shopping.

In one embodiment, the processor may generate a plurality of clusters 530 by clustering data 510 associated with a plurality of product queries into a plurality of clusters 530 . Then, the processor may extract one or more trend keywords 540 associated with at least one of a specific group or a specific period from one or more clusters among the plurality of clusters 530. For example, the processor extracts one or more trend keywords 540 from one or more clusters among the plurality of clusters 530, and features for one or more extracted trend keywords (e.g., a group that searches for the corresponding trend keyword in large numbers, Based on (gender, age, time period, etc.), specific groups and/or characteristic periods associated with one or more keywords 540 may be determined. That is, the processor may extract a trend associated with at least one of a specific group or a specific period based on the data 510 associated with the received plurality of product inquiries.

In addition, the processor uses the statistical topic embedding model 520 to cluster data 510 associated with a plurality of product queries into a plurality of clusters 530, and to select one or more trends from one or more clusters among the plurality of clusters 530. Keywords 540 can be extracted. Here, the statistical topic embedding model 520 may be a statistical or probability-based clustering model and/or an embedding model learned to cluster product queries with common shopping intent (or search intent, query intent) into one or more clusters. . As shown, the processor inputs data 510 associated with a plurality of product queries into the statistical subject embedding model 520, thereby generating a plurality of embedding data corresponding to each of the data 510 associated with the plurality of product queries. You can. The processor may cluster the generated plurality of embedding data into a plurality of clusters 530. Then, the processor may extract one or more trend keywords 540 from one or more of the plurality of clusters 530.

In one embodiment, the processor may use a statistical topic embedding model 520 to embed each product query into an embedding space based on data 510 associated with a plurality of product queries. Then, the processor may cluster the multiple product queries into a plurality of clusters 530 based on the distance between each product query in the embedding space. For example, the processor generates a cluster containing product queries that are close in distance based on a standard product query (e.g., a seed keyword) in the embedding space, thereby dividing the plurality of product queries into a plurality of clusters (530). ) can be clustered. Here, the standard product query may include a product query or keyword indicating a trend.

The processor may determine a reference product query to cluster the plurality of product queries into a plurality of clusters 530 . That is, the processor may determine a standard product query (or central product query) indicating a trend based on data 510 associated with a plurality of product queries. In one embodiment, the processor extracts popular keywords for a specific time of year (e.g., summer, winter, etc.) and/or for a specific group (e.g., a specific age, gender) based on data 510 associated with a plurality of product queries. And the extracted popular keywords can be determined as a standard product query. For example, the processor may discover keywords reflecting temporal characteristics based on data 510 associated with a plurality of product queries and determine them as standard product queries (e.g., seed keyword, main intent keyword). Here, the reference product query may include popular keywords corresponding to a specific point in time or period in the present or future, and accordingly, the processor can create a special exhibition corresponding to a specific point in time or period in the present or future desired by the operator. .

To determine baseline product inquiries, the processor organizes and analyzes time series data based on the inflow of product inquiries by gender/age group, and identifies products with high seasonality, trends, and inflow of product inquiries. You can select a query. In the case of product queries (or keywords) that are currently highly seasonal, the seasonal pattern may remain similar in the past and future, which are temporally adjacent to the present. Therefore, instead of time series data of product inquiries from a future point in time (e.g., 1 week later, 2 weeks later) where the inflow of product inquiries cannot be observed, the processor uses data from the same period in the past (e.g., the same date a year ago). , same season, etc.), the standard product query can be determined by analyzing time series data collected.

For example, the processor inputs valid product queries obtained through refining and/or filtering into the statistical topic embedding model 520, embeds each product query in the embedding space, and uses a standard product query (or keyword) (e.g. For example, a cluster containing product queries (or keywords) (e.g., 'maternity bag', 'newborn car seat', 'baby carrier', etc.) that are close to each other in the embedding space can be created around 'baby spacesuit'). You can. The processor then extracts one or more trend keywords 540 from the cluster and determines a specific population and/or specific time period associated with the cluster and/or the one or more trend keywords 540, thereby determining the specific population and/or Trends related to a specific period can be extracted.

One or more trend keywords 540 extracted from the cluster include keywords included in product queries included in the cluster, themes represented by the cluster, common characteristics of product queries contained in the cluster, the cluster, and It can include the associated user group, the time period associated with the group, etc. For example, one or more trend keywords 540 extracted from the 'baby spacesuit' cluster include 'baby bag' and 'newborn car seat', which are product queries (i.e., query sets of the cluster) included in the 'baby spacesuit' cluster. , 'baby carrier', etc. Additionally or alternatively, one or more trend keywords 540 extracted from the 'Baby Spacesuit' cluster may be selected from the topic of the 'Baby Spacesuit' cluster (e.g., common characteristics, categories, and , relevance, query intent, etc.), such as 'birth of a newborn', 'childcare', 'newborn', etc. Additionally or alternatively, one or more trend keywords 540 extracted from the 'baby spacesuit' cluster may include 'April', which is the time when many product inquiries included in the 'baby spacesuit' cluster were input. Additionally or alternatively, one or more trend keywords 540 extracted from the 'baby spacesuit' cluster include attributes related to age, gender, and marital status of a group of users who entered multiple product queries included in the 'baby spacesuit' cluster. May include people in their 30s, ‘women’, ‘married people’, etc. Based on one or more trend keywords 540 extracted from the cluster in this way, a trend title (or exhibition title) (for example, 'newborn baby theme') for the cluster may be generated.

Data 510 associated with multiple product queries may correspond to various types of data. For example, data 510 associated with a plurality of product queries may include various types of data such as text data, vector data, etc.

FIG. 6 is a diagram illustrating an example of determining a title 630 of a special exhibition indicating the theme of a trend based on a trend keyword 610 according to an embodiment of the present disclosure. A processor (eg, at least one processor of an information processing system) may determine a title 630 of a special exhibition indicating the subject of a trend based on one or more trend keywords 610 . For example, the processor may include a special exhibition title generator (or sales event title generator) 620 that generates the title of the special exhibition.

In one embodiment, the special exhibition title generator 620 may simply combine one or more input trend keywords 610 to generate a special exhibition title 630 indicating the theme of the trend. In another embodiment, the special exhibition title generator 620 may use a language model to convert one or more trend keywords 610 into a title of a special exhibition indicating the topic of the trend. In this case, the special exhibition title generator 620 may include a language model, and the language model learns to generate a sentence in which the intention of the input keyword (for example, a topic related to the keyword, etc.) appears based on the input keyword. It may be an artificial neural network model and/or a statistical model. In other words, the language model may be a model learned to recognize the relationships and meanings between a plurality of input trend keywords and generate sentences representing the topic of the trend.

As shown, the special exhibition title generator 620 receives trend keywords 610 including 'rash guard', 'swimsuit', 'beachwear', and 'June 27' as input, and creates the title of the special exhibition 'Water'. ‘Park, wait!’ (630) can be created. In one embodiment, the special exhibition title generator 620 may receive one or more trend keywords 610 as input and generate a plurality of sentences. In this case, the processor may determine at least one sentence among the plurality of sentences generated by the special exhibition title generator 620 as the title 630 of the special exhibition. For example, the processor outputs a plurality of sentences generated by the special exhibition title generation unit 620 as title candidates for the special exhibition through a user terminal (for example, the terminal of the special exhibition operator), and at least one of the plurality of output sentences In response to a user's (eg, special exhibition operator) selection of one sentence, at least one selected sentence may be determined as the title 630 of the special exhibition. Alternatively, the processor may receive a sentence directly input by the user through the user terminal and determine it as the title of the special exhibition.

FIG. 7 is a diagram illustrating an example of extracting one or more products 730 related to a trend from a plurality of products based on the title 710 of a special exhibition according to an embodiment of the present disclosure. A processor (e.g., at least one processor of an information processing system) may determine one or more products 730 associated with one or more trend keywords among a plurality of products as a special product, and generate a special exhibition including the special product. . In one embodiment, the processor may use a special product extraction model to extract one or more products 730 that match the title of the special exhibition from among a plurality of products and determine them as special products. Here, the special product extraction model may be a model learned to output one or more products that match the title of the input special exhibition. For example, the special product extraction model is the title of the special exhibition for each of the plurality of products based on information about each of the plurality of products (e.g., product title, category, tag, introduction, review, image, etc.) Calculate a matching score (e.g., distance in the embedding space), extract a predetermined number of products from the plurality of products based on the calculated matching score, and include the extracted predetermined number of products. It may correspond to a model learned to output a product list.

In another embodiment, the processor uses a special product extraction model to extract at least one product that matches the title of the special exhibition from among a plurality of products, and provides a list containing at least one product that matches the title of the special exhibition to the user terminal. It can be printed through . Thereafter, the processor may determine one or more products as the special product in response to the user's (for example, the operator of the special exhibition) selection of one or more products from the output list. That is, the processor can provide planned product candidates to the user through the user terminal, and can determine one or more products selected by the user through the user terminal among the planned product candidates as the planned product. Additionally or alternatively, the processor may determine that a product directly selected and/or entered by the user through the user terminal is a planned product.

As shown, the processor determines one or more products 730 output from the special product extraction model 720 among a plurality of products as a special product by inputting the title 710 of the special exhibition into the special product extraction model 720. And, you can create a special exhibition including the corresponding special product. Here, the title 710 of the special exhibition input into the special product extraction model 720 is not only the title text of the special exhibition itself, but also the target consumer (e.g., target group), topic, intention, subtitle, etc. of the special exhibition. May include information about hashtags, etc. For example, as the processor inputs the title of the special exhibition (710) ‘Wait for the water park’ into the special product extraction model (720), the special product extraction model (720) selects the title (710) of the special exhibition among a plurality of products. Products with a high matching score (i.e., products that match the title of the special exhibition) such as ‘swimsuits’, ‘swimming caps’, ‘aqua shoes’, ‘swimming bags’, and ‘water park season tickets’ can be printed. The processor may determine at least some of the output plurality of products as special products. For example, the processor outputs a list including a plurality of products output from the special product extraction model 720 as a special product candidate through the user terminal, and responds to the user's selection of at least some of the output plurality of products. Thus, the selected product can be determined as a planned product.

Figure 8 is a diagram showing an example of learning a special product extraction model according to an embodiment of the present disclosure. The above-described special product extraction model may include an embedding generation model learned to generate embedding data for each of a plurality of products and embedding data for the title of the special exhibition. Here, the embedding generation model may generate embedding data for each of a plurality of products based on information about each of the plurality of products being input. For example, the embedding generation model learns to generate embedding data for each of multiple products based on the product name, category, seller, tag, brand, manufacturer, product description, options, price, image, etc. It may be a model. In this case, the special product extraction model calculates a matching score for each of the plurality of products with the title of the special exhibition based on the embedding data for each of the plurality of products and the embedding data for the title of the special exhibition, and the calculated matching score Based on this, it may be an artificial neural network model and/or a statistical model learned to extract a predetermined number of products from a plurality of products and output a product list including the extracted predetermined number of products.

A processor (e.g., at least one processor of an information processing system) may receive data about a learning event and/or receive information about each of a plurality of products to generate and/or learn a product extraction model. there is. Here, the data on the learning special exhibition may include data on the previously actually held special exhibition and/or data on the special exhibition artificially created to learn the special product extraction model. Additionally, data on the special exhibition may include the title of the special exhibition, special product, target consumer, period of the special exhibition, etc. Additionally, learning events may be received from storage devices internal to and/or external to the information processing system.

The processor may learn a special product extraction model to output one or more products associated with the title of the special exhibition as the title of the special exhibition is input, based on data about the learning special exhibition and information about each of the plurality of products. In one embodiment, as the title of the special exhibition is input, the processor calculates a matching score with the title of the special exhibition for each of a plurality of products, and based on the calculated matching score, a predetermined number (e.g., For example, a product extraction model is trained to extract 10 products (e.g., products with matching score rankings from 1st to 10th) and output a product list containing a predetermined number of extracted products. You can.

In one embodiment, the processor uses an embedding data generation model (e.g., a language model such as BERT (Bidirectional Encoder Representations from Transformers)) to embed embedding data for the title of the learning project and products included in the learning project. You can create embedding data for products that are not included in the embedding data and learning plan. For example, the processor may generate embedding data for the title of the learning project by inputting the title of the learning project into the embedding data generation model, and products included in the learning project (e.g., products included in the learning project) By entering information about products), you can generate embedding data for products included in the learning plan, and enter products not included in the learning plan (for example, information about products not included in the learning plan). By doing so, it is possible to generate embedding data for products that are not included in the learning plan. Afterwards, the processor determines the matching score between the embedding data for the title of the learning exhibition and the embedding data for products included in the learning exhibition, and the embedding data for the title of the learning exhibition and the embedding data for products not included in the learning exhibition. You can learn a product extraction model using a triplet loss function that forces the matching score to be greater than the matching score between the two. Here, the matching score between embedding data may be inversely proportional to the distance between embedding data in the embedding space.

Additionally, in order to improve the performance of the planned product extraction model, a sampling process (e.g., negative sampling) may be performed to extract products that are not included in the learning plan as learning data. In one embodiment, the processor may sample, as learning data (i.e., negative data), a product that contains some similar features to a product included in the learning plan, from among a plurality of products not included in the learning plan. For example, when learning a product extraction model using the 'Razor' special exhibition as a learning exhibition, the processor selects a product in the same category as the razor (e.g., 'digital category') among a plurality of products not included in the 'Razor' special exhibition. ) can be extracted as a 'product not included in the learning plan' for learning the special product extraction model. In this case, the special product extraction model can be learned to extract special products by considering more detailed characteristics of the products.

As shown, the processor includes title A of the learning exhibition (also referred to as 'anchor') 812, product B included in the learning exhibition (i.e., planned product of the learning exhibition) (also referred to as 'positive') 814, and Each product C that is not included in the learning exhibition (i.e., an arbitrary product that is not a planning product in the learning exhibition) (also referred to as 'negative') 816 is input into the embedding generation model 810, so that the title A of the learning exhibition, Embedding data can be generated for product B and product C, respectively. Then, the processor embeds the embedding data (822) for title A of the learning exhibition in the direction in which the distance between the embedding data (822) for title A of the learning exhibition and the embedding data (824) for product B in the embedding space becomes closer. ) and the embedding data 826 for product C can be learned in a direction where the distance between them increases.

Figure 9 is a diagram illustrating an example in which a user (eg, an operator) creates a product promotion according to an embodiment of the present disclosure. A user terminal (eg, at least one processor of the user terminal) may provide a user (eg, operator) a user interface for creating a product promotion. In one embodiment, the user terminal may display the special exhibition topic candidate 912 on the display. For example, the user terminal may display candidates for a specific group and/or a specific time period targeted by the special exhibition on the display. Afterwards, the user terminal may receive a user input for selecting at least one of the special exhibition topic candidates 912 and transmit a request and/or data related thereto to the information processing system.

In one embodiment, the information processing system selects one from one or more clusters associated with a user-selected promotional topic (i.e., a specific group and/or specific time period selected by the user) from among a plurality of clusters generated by clustering a plurality of product queries. The above trend keywords can be extracted and output as trend keyword candidates 914 through the user terminal. Here, the one or more clusters may be a cluster containing product queries entered in large numbers by a specific group selected by the user and/or a cluster containing product queries entered in large numbers at a specific time. The user terminal may display trend keyword candidates 914 on the display, receive a user input for selecting at least one trend keyword from among the trend keyword candidates 914, and send a request and/or data related thereto to the information processing system. It can be sent to .

In one embodiment, the information processing system may generate one or more sentences based on at least one trend keyword selected by the user and output them through the user terminal as a title candidate 916 for a special exhibition. For example, the information processing system may generate one or more phrases or sentences based on at least one trend keyword selected by the user, as well as keywords associated with the topic of the special exhibition selected by the user, keywords associated with one or more clusters, and/or keywords similar to the trend keyword. can be generated and output through the user terminal as a title candidate 916 for a special exhibition. The user terminal may display title candidates 916 for a special exhibition on a display, receive a user input for selecting at least one title candidate 916 for a special exhibition, and send a request and/or data related thereto to the information processing system. Can be transmitted.

In one embodiment, the information processing system extracts at least one product that matches the title of the special exhibition selected by the user from among a plurality of products, and presents a list containing the extracted at least one product as a special product candidate 922 to the user. It can be printed through the terminal. Accordingly, the user terminal can display a list containing at least one product (i.e., a planned product candidate) on the display, and the user selects at least one product from the list (i.e., a planned product candidate) as a planned product. Input may be received and associated requests and/or data transmitted to an information processing system. Additionally, the information processing system may receive a request for creating and/or issuing a product promotion from the user terminal.

As described above, the information processing system may generate and/or issue a product exhibition including the special exhibition theme, trend keyword, title of the special exhibition, and/or special product selected by the user based on the received request and/or data. You can. Additionally or alternatively, the user may directly input the special exhibition theme, trend keyword, title of the special exhibition, and/or special product without selecting it from the candidate group. In this case, that is, the information processing system may create and/or issue a special product exhibition based on the special exhibition topic, trend keyword, title of the special exhibition, and/or special product input by the user. The information processing system can provide product promotions to consumers by creating and publishing product promotions.

As shown in the first operation 910, the user terminal is a special exhibition topic candidate 912, a period candidate ('this week', 'next week', 'in 2 weeks', 'in 3 weeks') and/ or group candidates ('early teens', 'late teens', 'early 20s', '30s', '40s', '50s', '60s', 'male', 'female') can be output through the user interface. In response to a user input selecting at least one of the printed special exhibition topic candidates 912 (e.g., '2 weeks later', '40s', 'women'), the user terminal selects the selected period and/or group and Trend keyword candidates extracted from the associated first cluster ('home decoration', 'lighting', 'curtain', 'interior') 914 and trend keyword candidates extracted from the second cluster ('hoodie', 'hood') , 'winter clothes') (914) can be output through the user interface. Here, the first cluster and/or the second cluster may be a cluster associated with product queries entered in large numbers during the selected period and/or a cluster associated with product queries entered in large numbers by the selected group.

The user terminal responds to a user input for selecting at least one of the output trend keyword candidates 914 ('home decoration', 'lighting', and 'interior'), and selects a special exhibition title candidate ( ‘How about decorating your home with lighting?’, ‘Interior items for this winter’) (916) can be printed through the user interface. In response to a user input for selecting at least one of the title candidates 916 for the special exhibition output in the first operation 910 ('How about decorating your home with lighting?'), the user terminal is displayed in the second operation 920. As shown, a special product candidate 922 including one or more products ('A stand (floor lamp)', 'B stand (table lamp)', 'C stand (table lamp') matching the title of the selected special exhibition. can be output. Based on a user input for selecting at least one product (‘A stand (floor lamp)’) from among the output special product candidates 922, a product special exhibition including the selected at least one product may be created. Additionally, the corresponding product promotion may be issued and provided to consumers according to a user input requesting the issuance of the created product promotion.

FIG. 10 is a diagram illustrating an example in which a user (eg, an operator) is provided with one or more product promotion candidates 1000 according to an embodiment of the present disclosure. According to the above-described embodiments, the information processing system can generate one or more product promotions. The information processing system may provide one or more product promotions created as product promotion candidates 1000 to the user through the user terminal. The user terminal may display information about one or more product promotions corresponding to the product promotion candidate 1000 on the display.

As shown, the user terminal can output 'winter health care food', 'winter school uniform styling', 'prepare New Year's gifts in advance', and 'let's go to the ski resort' as product promotion candidates 1000 through the user interface. For example, the user terminal is the target consumer for each product promotion candidate (e.g., '57-63 year old male', '18 year old female', '45-55 year old female', '20-30 male') , period, image, hashtags ('#health', '#elderly', '#student', etc.), title of special exhibition, etc. can be printed through the user interface. Additionally, the user terminal may display the detailed configuration of the selected product promotion on the display in response to a user input for selecting at least one product promotion from among the product promotion candidates 1000.

As the user selects at least one product promotion among the product promotion candidates 1000 and performs a request to issue the selected product promotion, the corresponding product promotion may be created and issued and provided to consumers. In Figure 10, the user terminal outputs four product promotion candidates, but the present invention is not limited to this and may output a different number of product promotion candidates.

The above-described method may be provided as a computer program stored in a computer-readable recording medium for execution on a computer. The medium may continuously store a computer-executable program, or may temporarily store it for execution or download. In addition, the medium may be a variety of recording or storage means in the form of a single or several pieces of hardware combined. It is not limited to a medium directly connected to a computer system and may be distributed over a network. Examples of media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, And there may be something configured to store program instructions, including ROM, RAM, flash memory, etc. Additionally, examples of other media include recording or storage media managed by app stores that distribute applications, sites or servers that supply or distribute various other software, etc.

The methods, operations, or techniques of this disclosure may be implemented by various means. For example, these techniques may be implemented in hardware, firmware, software, or a combination thereof. Those skilled in the art will understand that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented in electronic hardware, computer software, or combinations of both. To clearly illustrate this interchange of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and design requirements imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementations should not be interpreted as causing a departure from the scope of the present disclosure.

In a hardware implementation, the processing units used to perform the techniques may include one or more ASICs, DSPs, digital signal processing devices (DSPDs), programmable logic devices (PLDs). ), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic devices, and other electronic units designed to perform the functions described in this disclosure. , a computer, or a combination thereof.

Accordingly, the various illustrative logical blocks, modules, and circuits described in connection with this disclosure may be general-purpose processors, DSPs, ASICs, FPGAs or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or It may be implemented or performed as any combination of those designed to perform the functions described in. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, such as a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other configuration.

For firmware and/or software implementations, techniques include random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), and PROM ( on computer-readable media such as programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, compact disc (CD), magnetic or optical data storage devices, etc. It may also be implemented as stored instructions. Instructions may be executable by one or more processors and may cause the processor(s) to perform certain aspects of the functionality described in this disclosure.

Although the above-described embodiments have been described as utilizing aspects of the presently disclosed subject matter in one or more standalone computer systems, the disclosure is not limited thereto and may also be implemented in conjunction with any computing environment, such as a network or distributed computing environment. . Furthermore, aspects of the subject matter of this disclosure may be implemented in multiple processing chips or devices, and storage may be similarly effected across the multiple devices. These devices may include PCs, network servers, and portable devices.

Although the present disclosure has been described in relation to some embodiments in this specification, various modifications and changes may be made without departing from the scope of the present disclosure as can be understood by a person skilled in the art to which the invention pertains. Additionally, such modifications and changes should be considered to fall within the scope of the claims appended hereto.

100: User
110: user terminal
120: screen

Claims (15)

In a product promotion creation method performed by at least one computing device,
Receiving data associated with a plurality of product queries;
Clustering data associated with the plurality of product queries into a plurality of clusters with the same or similar user intent;
extracting one or more trend keywords associated with at least one of the specific group or a specific period from one or more of the plurality of clusters; and
Step of creating a product special exhibition including special products related to the extracted trend
Including,
The step of creating a product special exhibition including special products related to the extracted trend is,
determining one or more products related to the one or more trend keywords among a plurality of products as the special product; and
Including the step of creating a product special exhibition including the special product,
The step of determining one or more products related to the one or more trend keywords among the plurality of products as the planned product,
determining a title of a special exhibition representing the theme of the trend based on the one or more trend keywords; and
Based on the title of the special exhibition, determining one or more products among the plurality of products as the special product,
Based on the title of the above special exhibition. The step of determining one or more products among the plurality of products as the planned product,
Extracting one or more products matching the title of the special exhibition from among the plurality of products using a special product extraction model; and
Step of determining the extracted one or more products as the planned product
Method for creating a product promotion, including.
delete According to paragraph 1,
The step of clustering the data associated with the plurality of product queries into a plurality of clusters with the same or similar user intent,
A product promotion creation method comprising clustering data associated with the plurality of product queries into a plurality of clusters using a statistical topic embedding model.
In a product promotion creation method performed by at least one computing device,
Receiving data associated with a plurality of product queries;
Clustering data associated with the plurality of product queries into a plurality of clusters with the same or similar user intent;
extracting one or more trend keywords associated with at least one of the specific group or a specific period from one or more of the plurality of clusters; and
Step of creating a product special exhibition including special products related to the extracted trend
Including,
The step of creating a product special exhibition including special products related to the extracted trend is,
determining one or more products related to the one or more trend keywords among a plurality of products as the special product; and
Including the step of creating a product special exhibition including the special product,
The step of determining one or more products related to the one or more trend keywords among the plurality of products as the planned product,
determining a title of a special exhibition representing the theme of the trend based on the one or more trend keywords; and
Based on the title of the special exhibition, determining one or more products among the plurality of products as the special product,
The step of determining one or more products among the plurality of products as the special product based on the title of the special exhibition,
Extracting one or more products matching the title of the special exhibition from among the plurality of products using a special product extraction model;
Outputting a list including at least one product matching the title of the special exhibition through a user terminal; and
In response to a user's selection of the one or more products from the output list, determining the one or more products as the special product.
Method for creating a product promotion, including.
delete According to paragraph 1,
The step of determining a title of a special exhibition representing the theme of the trend based on the one or more trend keywords,
A method for creating a product special exhibition, including converting the one or more trend keywords into a title of the special exhibition representing the theme of the trend using a language model.
delete According to paragraph 1,
The special product extraction model calculates a matching score with the title of the special exhibition for each of the plurality of products based on information about each of the plurality of products, and based on the calculated matching score, calculates a matching score for the plurality of products. A method of generating a product exhibition, which is learned to extract a predetermined number of products from among the products and output a product list including the extracted predetermined number of products.
delete In a method of learning a product extraction model performed by at least one computing device,
Receiving data about the learning plan;
Receiving information about each of a plurality of products; and
Learning the special product extraction model to output one or more products associated with the title of the special exhibition from the title of the special exhibition, based on data about the learning special exhibition and information about each of the plurality of products.
Including,
The step of learning the special product extraction model to output one or more products from the plurality of products from the title of the special exhibition based on data about the learning special exhibition and information about each of the plurality of products,
The matching score between the embedding data for the title of the learning project and the embedding data for products included in the learning project is the match score between the embedding data for the title of the learning project and the embedding data for products not included in the learning project. A method of learning a special product extraction model, including the step of learning the special product extraction model using a triplet loss function that forces the matching score to be greater than .
According to clause 10,
The step of learning the special product extraction model to output one or more products related to the title of the special exhibition from the title of the special exhibition,
As the title of the special exhibition is input, a matching score for each of the plurality of products with the title of the special exhibition is calculated, and based on the calculated matching score, a predetermined number of products are extracted from the plurality of products, , Learning the special product extraction model to output a product list including the extracted predetermined number of products.
delete A computer program stored in a computer-readable recording medium for executing the method according to any one of claims 1, 3, 4, 6, 8, 10, and 11 on a computer.
As an information processing system,
communication module;
Memory; and
At least one processor connected to the memory and configured to execute at least one computer-readable program included in the memory
Including,
The at least one program is,
Receive data associated with a plurality of product queries,
Clustering data associated with the plurality of product queries into a plurality of clusters with the same or similar user intent,
Extracting one or more trend keywords associated with at least one of the specific group or specific period from one or more of the plurality of clusters,
Contains commands for creating a product promotion including special products related to the extracted trend,
Creating a product special exhibition including special products related to the extracted trend is,
Determining one or more products related to the one or more trend keywords among the plurality of products as the planned product,
Including creating a product special exhibition including the special product,
Determining one or more products associated with the one or more trend keywords among the plurality of products as the special product,
Determining a title for a special exhibition representing the theme of the trend based on the one or more trend keywords,
Based on the title of the special exhibition, determining one or more products from among the plurality of products as the special product,
Based on the title of the above special exhibition. Determining one or more products from among the plurality of products as the special product,
Using a special product extraction model, extract one or more products that match the title of the special exhibition from among the plurality of products,
Determining the one or more extracted products as the planned product
Information processing system, including.
As an information processing system,
communication module;
Memory; and
At least one processor connected to the memory and configured to execute at least one computer-readable program included in the memory
Including,
The at least one program is,
Receive data about learning events,
Receive information about each of a plurality of products,
Based on the data about the learning special exhibition and information about each of the plurality of products, it includes instructions for learning a special product extraction model to output one or more products related to the title of the special exhibition from the title of the special exhibition,
Based on the data about the learning special exhibition and information about each of the plurality of products, learning the special product extraction model to output one or more products from the plurality of products from the title of the special exhibition includes:
The matching score between the embedding data for the title of the learning project and the embedding data for products included in the learning project is the match score between the embedding data for the title of the learning project and the embedding data for products not included in the learning project. An information processing system comprising learning the product extraction model using a triplet loss function that forces the matching score to be greater than .

Images