KR20210112258A - Method and apparatus for providing personalized recommendation service for offline purchase - Google Patents

Abstract

The present invention relates to a personalized recommendation service providing method for offline purchase for a user to try on and purchase a coordinated cloth when visiting an offline store or get a recommendation or find a cloth fitting nicely in the store to calculate a coordinate score on whether it goes well with an existing cloth and evaluate fitness in advance so as to minimize a time-consuming process such as trying on clothes and the like and increase offline purchase experience, thereby increasing consumers' purchase satisfaction and increasing a revisit rate.

Description

METHOD AND APPARATUS FOR PROVIDING PERSONALIZED RECOMMENDATION SERVICE FOR OFFLINE PURCHASE

The present invention relates to a method and apparatus for providing a personalized recommendation service for offline purchase.

Currently, as interest in fashion increases, many people own various clothing items, and the desire to purchase clothing items also increases, so that industries or services related to online shopping or offline shopping are variously developed or provided. People can satisfy their purchasing desires by selectively purchasing desired clothing items online or offline according to circumstances or preferences.

However, in the case of conventional online shopping, even if the buyer carefully decides to purchase by referring to the information provided online, that is, product image information, the seller's product description, and the buyer's product reviews, the product actually received There was a big difference from the information provided online, so it was often the case that the purchase failed. In addition, there is a problem that the return and exchange process according to the purchase failure is also cumbersome and consumes time and effort.

On the other hand, in the case of the conventional offline shopping, unlike online shopping, since the buyer directly experiences the product in the offline store and then purchases, the difference between the buyer's expectation and the actual experience is not large, so the satisfaction with the shopping is high. However, since offline purchase consumes a lot of time, there is a problem in that there is a physical and time constraint on the purchaser. Specifically, buyers have to spend a lot of time to purchase products, such as the time the buyer travels to the offline store and the time to browse the product to select the desired product. occurred frequently. This has a problem in that it may make the buyer feel like a waste of time, negatively remember the experience of the offline store, and feel reluctant to revisit.

Accordingly, there is a need for a method for enabling users to efficiently shop by taking only the advantages of online shopping and offline shopping while solving the problems of online shopping and offline shopping.

Republic of Korea Patent Publication No. 10-2016-0064379, 2016.06.08.

An object of the present invention for solving the above problems is to provide a method and apparatus for providing a personalized recommendation service for offline purchase.

The problems to be solved by the present invention are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the following description.

A method of providing a personalized recommendation service for offline purchase according to an aspect of the present invention for solving the above-described problems includes: obtaining information about clothing owned by a user related to clothing owned by a user from a user terminal; extracting usage information on the owned clothing; extracting a purchase necessary product from among the owned clothing based on the use information; searching for at least one first offline store for the purchase necessary product; and 1 providing a search result to the user terminal, wherein the use information includes the number of times and purchase date of the owned clothing.

In the present invention, in the first offline store search step, based on the distance from the user's current location, the at least one first offline store is arranged according to the order of the closest distance to the user, and the product required to be purchased When extracting, if a plurality of products needing to be purchased are extracted, the first offline store search step may include searching for the at least one first offline store in an order in which the distance to the user is close to the user based on the distance from the user's current location , and based on stock information for each of the plurality of purchase-needed products, stock the at least one first offline store sorted according to the order of closest distance to the user among the plurality of purchase-necessary products. It can be rearranged according to the order of the number of items that need to be purchased.

In the present invention, the method may further include providing, to the user terminal, display information of the product required to be purchased in a first offline store selected by the user terminal among the searched first offline stores.

In the present invention, the method may further include receiving an expected visit time for a first offline store selected by the user terminal among the searched first offline stores from the user terminal.

In the present invention, the steps of analyzing the characteristic information on the owned clothes based on the owned clothes information, specifying the user in a multidimensional space indicating the degree of similarity between users based on the analysis result, the multidimensional space recommending a product (hereinafter referred to as a recommended product) suitable for the product required to be purchased based on information on clothing owned in a user group located in a space adjacent to the user on the screen; searching for at least one second offline store for the recommended product and providing a second search result to the user terminal.

In the present invention, the step of analyzing the characteristic information includes extracting at least one clothing factor representing an attribute of clothing from the owned clothing information, and obtaining characteristic information on the owned clothing information based on the at least one clothing factor. analyzing and calculating the characteristic value of the user, wherein the user specifying step includes: obtaining coordinate information corresponding to the characteristic value in the multidimensional space; and specifying the user in the obtained coordinate information may include the step of

In the present invention, in the second offline store search step, based on the distance to the first offline store, the at least one second offline store is sorted according to the order in which the distance to the first offline store is close to the first offline store, When recommending a recommended product, if a plurality of recommended products are recommended, the step of searching for the first offline store may include setting the at least one second offline store with a first offline store based on a distance from the first offline store. The plurality of the at least one second offline store arranged in the order in which the distance to the first offline store is close to the first offline store is arranged according to the order of the closest distance, and based on the stock information for each of the plurality of recommended products. Among the recommended products of , the number of recommended products in stock may be rearranged according to the order of the largest number.

In the present invention, the steps of determining whether there is owned clothing similar to the recommended product among the owned clothing of the user, and when it is determined that there is similar owned clothing, providing a coordination score between the similar owned clothing and the purchased product It may further include the step of

In the present invention, the method may further include: obtaining image information of a desired product to be purchased from the user terminal; and providing a coordination recommendation to the user terminal based on the owned clothing information and image information of the desired product to be purchased. can

An apparatus according to another aspect of the present invention for solving the above problems includes a memory storing one or more instructions, and a processor executing the one or more instructions stored in the memory, wherein the processor includes the one or more instructions by executing the steps of obtaining, from the user terminal, owned clothing information related to the user's owned clothing, extracting usage information about the owned clothing based on the owned clothing information, from among the owned clothing based on the use information. The steps of extracting a purchase necessary product, searching for at least one first offline store for the purchase necessary product, and providing a first search result to the user terminal are performed.

In addition to this, other methods for implementing the present invention, other apparatuses, other systems, and computer-readable recording media for recording a computer program for executing the method may be further provided.

According to the present invention as described above, based on the clothes the user owns, by extracting clothes that need to be repurchased (products that need to be purchased) from among the clothes owned by the user, and presenting offline stores where the clothes can be purchased, the user can You can quickly purchase fashion items offline.

In addition, by accurately analyzing the user's characteristics (ie, taste, preference, etc.) based on the clothes the user owns, it is possible to recommend a personalized coordination considering the user's characteristics.

In addition, by collecting and analyzing the clothing information owned by multiple users to determine the characteristics (ie, taste, preference, etc.) for each user, and grouping users with the same or similar characteristics through this, the preferred coordination of users in each group pattern can be discerned. In addition, since users in each group have the same or similar characteristics, by using the preferred coordination of users in the same group or adjacent groups, a fashion item suitable for the user's purchase necessary product is extracted, and this is extracted as a recommended product. By presenting it to the user, it is possible to recommend personalized and customized coordination.

In addition, by presenting an offline store where the recommended product can be purchased in connection with the offline store where the product to be purchased can be purchased, the user can quickly purchase the recommended product along with the necessary product, thereby enabling efficient offline shopping.

Effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a diagram schematically illustrating a system for providing a personalized recommendation service for offline purchase according to the present invention.
2 is a flowchart of a method for providing a personalized recommendation service for offline purchase of a product that needs to be purchased according to the present invention.
3 is a flowchart of a method for providing a personalized recommendation service for offline purchase of a recommended product according to the present invention.
4 is a flowchart of a specific method of step S210 of FIG. 3 .
5 is a flowchart of a specific method of step S220 of FIG. 3 .
6 is a diagram illustrating an example of a multidimensional space indicating the degree of similarity between users according to the present invention and a process of creating a user group by grouping according to the degree of similarity of users within the space.
7 is a diagram schematically illustrating a configuration of an apparatus for performing a method for providing a personalized recommendation service for offline purchase according to the present invention.

Advantages and features of the present invention and methods of achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and only these embodiments allow the disclosure of the present invention to be complete, and those of ordinary skill in the art to which the present invention pertains. It is provided to fully understand the scope of the present invention to those skilled in the art, and the present invention is only defined by the scope of the claims.

The terminology used herein is for the purpose of describing the embodiments and is not intended to limit the present invention. In this specification, the singular also includes the plural unless specifically stated otherwise in the phrase. As used herein, “comprises” and/or “comprising” does not exclude the presence or addition of one or more other components in addition to the stated components. Like reference numerals refer to like elements throughout, and "and/or" includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various elements, these elements are not limited by these terms, of course. These terms are only used to distinguish one component from another. Accordingly, it goes without saying that the first component mentioned below may be the second component within the spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used herein will have the meaning commonly understood by those of ordinary skill in the art to which this invention belongs. In addition, terms defined in a commonly used dictionary are not to be interpreted ideally or excessively unless specifically defined explicitly.

In order to solve the problems of the conventional online shopping or offline shopping, the present invention provides a value of direct experience through offline shopping and solves the pain point of offline shopping that a lot of time is consumed until purchasing a product. To this end, by collecting and analyzing various information about the purchase experience of the buyer, it is possible to present and recommend the necessary products to the buyer, as well as to provide offline store information where they can purchase the product. Accordingly, through offline shopping, the buyer acquires the value of direct experience that cannot be experienced through online shopping, and is provided with a product that suits their needs or their taste and offline store information where they can purchase the product. It allows you to shop quickly and efficiently even offline.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a diagram schematically illustrating a system for providing a personalized recommendation service for offline purchase according to the present invention.

2 is a flowchart of a method for providing a personalized recommendation service for offline purchase of a product that needs to be purchased according to the present invention.

3 is a flowchart of a method for providing a personalized recommendation service for offline purchase of a recommended product according to the present invention.

4 is a flowchart of a specific method of step S210 of FIG. 3 .

5 is a flowchart of a specific method of step S220 of FIG. 3 .

6 is a diagram illustrating an example of a multidimensional space indicating the degree of similarity between users according to the present invention and a process of creating a user group by grouping according to the degree of similarity of users within the space.

7 is a diagram schematically illustrating a configuration of an apparatus for performing a method for providing a personalized recommendation service for offline purchase according to the present invention.

Referring to FIG. 1 , a system 1 (hereinafter referred to as a system) for providing a personalized recommendation service for offline purchase according to an embodiment of the present invention includes a server 10, a user terminal 20, and an offline store terminal ( 30) and a communication network 40 may be included.

In the system 1 according to the present invention, the subject of the invention may be a computing device or a server device, and specifically, the server 10 may be an execution subject, but is not limited thereto and in some cases the user terminal 20 Alternatively, the offline store terminal 30 may be the system 1 performing entity.

The server 10 is a computing device for providing a method for providing a personalized recommendation service for offline purchase according to an embodiment of the present invention. The server 10 provides a personalized recommendation based on the clothing it owns to users through the user terminal 20 . Various functions can be performed to provide services. For convenience of explanation, although one server 10 is illustrated in FIG. 1 , the system 1 according to an embodiment of the present invention may include one or more servers. In addition, the server 10 may be configured as a cloud server.

The user terminal 20 is a terminal device of a user (ie, a client) using a personalized recommendation service based on user owned clothing provided through the server 10 , for example, a smart phone, a tablet PC, a desktop, a notebook computer, and a smart TV. It may be various computing devices, such as.

The user terminal 20 may include an application that provides a personalized recommendation service based on clothes owned by the user. Accordingly, the user may be provided with a personalized recommendation service based on the user's own clothing through the application of the user terminal 20 .

Here, the user may mean a buyer who receives the personalized recommendation service and purchases a product in an offline store.

The offline store terminal 30 is a terminal device of an offline store associated with the personalized recommendation service provided to the user by the server 10, for example, various computing devices such as a smart phone, a tablet PC, a desktop, a notebook computer, and a smart TV. can be

The offline store terminal 30 may provide various types of information related to the offline store to the server 10 , such as a store location, a type of owned product, and inventory information of the owned product.

The server 10 may recommend an optimal offline store in which the user can purchase the recommended product based on the type and inventory information of the product owned by each connected offline store.

The server 10 may update the inventory information of the stock provided from the offline store 30 in real time. For example, the server 10 may receive an image and stock information of a holding product through a stock input application provided to an employee of an affiliated offline store. Through this, small merchants can upload information such as inventory information to the online server 10 by using a simple inventory item input service (ie, an inventory input application) without new infrastructure investment in the offline store. Also, for example, the server may acquire inventory information in real time by interworking with the inventory management program of each connected offline store.

The communication network 40 enables transmission and reception of various information between the server 10 , the user terminal 20 , and the offline store terminal 30 . The communication network 40 may use various types of communication networks, for example, WLAN (Wireless LAN), Wi-Fi (Wi-Fi), Wibro (Wibro), Wimax (Wimax), HSDPA (High Speed Downlink Packet Access), such as A wireless communication method or a wired communication method such as Ethernet, xDSL (ADSL, VDSL), HFC (Hybrid Fiber Coax), FTTC (Fiber to The Curb), FTTH (Fiber To The Home) may be used.

However, the communication network 40 is not limited to the communication method presented above, and may include all types of communication methods that are well-known or to be developed in the future in addition to the above-described communication methods.

Hereinafter, using the server 10, the user terminal 20, and the offline store terminal 30 included in the system 1, a detailed process of providing a method for providing a personalized recommendation service for offline purchase will be described. .

Referring to FIG. 2 , the method of providing a personalized recommendation service for offline purchase performed by the server 10 according to an embodiment of the present invention includes the steps of: S110), extracting usage information on the owned clothing based on the owned clothing information (S120), extracting a purchase necessary product from among the owned clothing based on the use information (S130), the purchase necessary product It may include the step of searching for at least one first offline store for (S140) and the step of providing the first search result to the user terminal (S150). Hereinafter, detailed description of each step is described.

The server 10 may obtain information about the clothes owned by the user related to the clothes owned by the user from the user terminal 20 ( S110 ).

Here, the owned clothing refers to the user's pre-owned clothing items, and is used to encompass all of the user's own clothing and various clothing items related to the clothing. For example, the clothes owned by the user may include clothes such as tops, bottoms, outerwear, and underwear, and other shoes, bags, hats, and accessories.

In an embodiment, the user terminal 20 may directly receive information on owned clothing related to the clothing owned by the user himself/herself from the user. Here, the owned clothing information may include the type, color, shape and shape (image) of the owned clothing, material, fabric pattern, price, size, number of wearing, purchase date, and the like. In this case, the server 10 may acquire information about the clothing directly input from the user through the user terminal 20 .

In this case, the type, color, shape and shape (image), material, fabric pattern, price, size, purchase date, etc. of the owned clothing among the owned clothing information are fixed information, and the initially input value may be maintained as it is. On the other hand, the number of wears among the owned clothing information is variable information, and the initially input value is a default value (eg, 0) and may be updated whenever the user wears the corresponding clothing.

For example, when a user wears a specific owned clothing from among the owned clothing, if the user selects the worn owned clothing along with the wearing date through the application included in the user terminal 20, the number of wearing the corresponding owned clothing is 1 It can be updated by incrementing.

In another embodiment, the user terminal 20 may receive the user's clothing information as an image (photo, video, etc.) from the user. For example, the user may input a photo or image captured by a camera of clothes, shoes, bags, and the like, into the user terminal 20 . In this case, the server 10 obtains an image including the user's clothing information from the user terminal 20, and from the acquired image information on clothing (eg, the type, color, shape, and shape of the owned clothing) image), material, fabric pattern, size, etc.) can be recognized to obtain information on the user's own clothing. For example, the server 10 uses an object detection algorithm based on deep learning (eg, CNN; convolution neural network), the type, color, Information such as shape and shape (image), material, fabric pattern, size, etc. may be recognized, and the recognized information may be acquired as information on the user's own clothing. In this case, the purchase date among the owned clothing information may be set as a default date on which the owned clothing information is input, and may then be corrected to the actual purchase date by the user.

In another embodiment, the user terminal 20 recognizes clothing product code information (eg, barcode, QR code, etc.) provided to the clothing, and from the recognized clothing product code information, the manufacturer and product information (type of clothing, color, etc.) , shape and shape (image), material, fabric pattern, price, size, etc.) can be acquired. Alternatively, the user terminal 20 may recognize clothing product code information provided to clothing, and transmit the recognized clothing product code information to the server 10 .

In this case, the server 10 may build a database for storing and managing clothing product information. In this case, when the server 10 obtains the clothing product code information from the user terminal 20, the manufacturer and product information corresponding to the clothing product code information (type, color, shape, and shape ( image), material, fabric pattern, price, size, etc.), it is possible to obtain the user's clothing information. Also, in building the database, the server 10 may obtain clothing product information from a clothing manufacturer or a sales company and convert it into a database. In this case, the purchase date among the owned clothing information may be set as a default date on which the owned clothing information is input, and may then be corrected to the actual purchase date by the user.

In another embodiment, when the user purchases clothing or clothing-related items from a clothing seller such as an online shopping mall, the user terminal 20 obtains information on the clothing or clothing-related items purchased by the user from the clothing seller, , it can be transmitted to the server 10 . For example, the user terminal 20 may obtain image information about clothes or clothes-related items purchased by the user from a corresponding clothes seller, and transmit it to the server 10 . Alternatively, the user terminal 20 obtains identification information (eg, identification information of a corresponding clothing seller and identification information on clothing products, etc.) on the clothing or clothing-related items purchased by the user, and transmits the identification information to the server 10 . ) can be transmitted. In this case, the server 10 may acquire the corresponding clothing or clothing-related item information from the corresponding clothing seller based on the identification information.

As described above, the server 10 may acquire information on the clothing owned by the user through the user terminal 20 in various ways. In this way, the owned clothing information obtained from each user may be used as big data.

In one embodiment, the server 10 may build a big data pipeline in order to collect the user's clothing information from each user and analyze, process and use it according to the purpose. In addition, the server 10 may use artificial intelligence technology, such as machine learning, deep learning, and the like, in order to analyze and utilize the user's clothing information collected from each user according to the purpose. In this case, the server 10 may perform learning and build a learning model by using the user's clothing information collected from each user as learning data. In using the user's clothing information as big data or learning data as described above, the system 1 according to an embodiment of the present invention may configure and use a separate server in addition to the server 10 .

In this way, by using the owned clothing information obtained from each user as big data, the server 10 is a customized service that recommends a coordinated fit for each situation, clothing, preference, taste, etc. to users who use the personalized recommendation service. can provide

The server 10 may extract usage information about the user's own clothing based on the owned clothing information obtained in step S110 (S120).

Here, the usage information may include the number of times of wearing the owned clothing and a purchase date. That is, the server 10 may utilize the number of times worn and the date of purchase among the owned clothing information as usage information.

According to an embodiment, the server 10 obtains the user's clothing information from the user terminal 20 of the users at regular intervals (eg, daily, weekly, monthly, etc.) By reflecting the information, use information for the user's own clothing information may be updated.

The server 10 may extract a product that needs to be purchased from among the owned clothing based on the use information extracted in step S120 (S130).

Specifically, the server 10 stores clothing owned by the user in which the number of times of wearing is equal to or greater than the preset reference number, and the period between the purchase date and the current date (ie, the period elapsed from the purchase date) is equal to or greater than the preset reference period. can be extracted as a product that needs to be purchased.

If the reference number of times is 100 and the reference period is 5 years, for example, the number of times of wearing the owned clothing A is 20 times, the period elapsed from the purchase date is 3 years, the number of times of wearing the owned clothing B is 10 times, the purchase date If the elapsed period is one year, the number of times of wearing the owned clothing C is 120 times, and the reference period is 5 years, the server 10 may extract the owned clothing C as a purchase necessary product.

According to an embodiment, the server 10 may extract the possessed clothing as a purchase-needed product if the number of times of wearing is equal to or greater than a preset maximum number of times even if the period elapsed from the purchase date does not exceed the reference period. For example, if the number of wears exceeds the maximum number of times, for example, 200 times, even though it has not been 5 years since the user purchased the owned clothing A, the server 10 may extract the owned clothing A as a purchase necessary product. .

According to an embodiment, even if the number of wearing times does not exceed the reference number of times, if the period elapsed from the purchase date is equal to or greater than a preset maximum period, the server 10 may extract the corresponding owned clothing as a purchase necessary product. For example, even if the number of times the user wears the owned clothing A does not exceed 100 times, if the purchase period exceeds the maximum period, for example, 8 years, the server 10 extracts the owned clothing A as a purchase necessary product. can

In this way, the server 10 may provide a personalized recommendation service to the user by extracting, from among the clothes owned by the user, clothes determined to require repurchase based on the number of times worn or the date of purchase as a purchase necessary product and presenting them to the user.

Meanwhile, the server 10 may extract a product that needs to be discarded from the owned clothing based on the usage information and present it to the user.

Specifically, if the period elapsed from the purchase date is equal to or greater than the maximum period and the number of wears is less than or equal to a preset minimum number of times, the server 10 may extract the possessed clothing as a product requiring disposal and present it to the user. For example, if the number of times the user has purchased the owned clothing A has not exceeded the minimum number of times, for example, 10 times, even after the maximum period, for example, 8 years, has passed since the user has purchased the owned clothing A, the server 10 discards the owned clothing A It can be extracted as a necessary product. Accordingly, the user may dispose of the owned clothing that does not fit his/her taste (which is not frequently worn).

In the above, the server 10 has been described as extracting one purchase-needed product and discarded product, but the present invention is not limited thereto, and a plurality of purchase-needed products and discarded products may be extracted.

The server 10 may search for at least one first offline store for the necessary purchase product extracted in step S130 (S140).

According to an embodiment, the server 10 may sort the at least one first offline store according to the order of the closest distance to the user, based on the distance from the user's current location.

In this case, the server 10 may extract, as the first offline store, an offline store having the necessary purchase product among numerous offline stores linked to the personalized recommendation service. That is, the server 10 does not extract the offline store that does not have the purchase necessary product as the first offline store.

The extracted first offline stores are sorted and searched in the order of distance from the user's location, respectively.

For example, the first offline store A, B, and C all have the necessary purchase product A. The first offline store A is 1 km away from the user's location, and the first offline store B is the user's location. If the distance to is 5 km and the distance from the user's position to the first offline store C is 0.8 km, the server 10 calculates the calculated distance in the shortest order, that is, the first offline store C, A, and B in the order of can be sorted by

According to an embodiment, when the server 10 extracts a plurality of purchase-needed products in step S130, the server 10 connects the at least one first offline store with the user based on the distance from the user's current location. Sort the at least one first offline store arranged in the order in which the distance from the user is close to each other based on the inventory information for each of the plurality of purchase-needed products It may be rearranged according to the order in which the number of products that are in stock among the products that need to be purchased is greater.

In this case, the server 10 may extract, as the first offline store, an offline store having at least one purchase necessary product among the plurality of purchase necessary products among numerous offline stores linked to the personalized recommendation service. That is, the server 10 does not extract the offline store that does not have any of the plurality of purchase necessary products as the first offline store.

The extracted first offline stores are sorted and searched in the order of distance from the user's location, respectively. Specifically, scores may be given in the order of the closest distance to the calculated user's location, and the scores may be sorted in the order of the highest.

For example, the first offline stores A, B, and C all have at least one of the plurality of purchase-necessary products. The first offline store A is 1 km away from the user's location, and the first offline store B If the distance from the user's location is 5 km, and the first offline store C has a distance of 0.8 km from the user's position, the server 10 gives 3 points to the first offline store C with the shortest calculated distance, , 2 points are given to the first offline store A with the second shortest calculated distance, and 1 point is given to the first offline store B with the longest calculated distance, so that the first offline store C, A with the highest score given , can be sorted in the order of B.

In addition, the three first offline stores arranged based on the distance are rearranged according to the order in which the number of products required to be purchased in stock among the plurality of products required to be purchased is greater. Specifically, the final score is calculated by giving weights according to the number of products that are in stock to the scores given to each of the first offline stores A, B, and C, and the calculated final scores can be rearranged in the highest order have.

That is, when 5 products (A, B, C, D, E) that need to be purchased are extracted, the first offline store A has inventory for products A, B, C, D, and E from among the 5 products that need to be purchased. , and the first offline store B has stock for products A, B, and C that need to be purchased among the 5 products that need to be purchased, and the first offline store C holds only the inventory for products that need to be purchased A, If there is, since the number of products required to be purchased in stock of each of the first offline stores A, B, and C is 5, 3, and 1, weights may be assigned based thereon.

For example, the first offline store A has a distance of 1 km from the user's location, the first offline store B has a distance of 5 km from the user's location, and the first offline store C has a distance of 0.8 from the user's position. km, the first offline store C, A, and B are sorted in the order, and if 3 points, 2 points, and 1 point are given to each of the first offline stores C, A, and B, the first offline store A has the stock Since the number of products required to be purchased is 5, the number of products required to be purchased in the first offline store B is 3, and the number of products required to be purchased in stock is 1 in the first offline store C, the above A weight of 3 points is given to the first offline store A having the largest number of items to be purchased in stock, and 2 points are given to the first offline store B having the second largest number of items to be purchased in stock, and A weight of 1 point may be assigned to the first offline store C having the smallest number of products required to be purchased in stock. Accordingly, since the calculated final scores of the first offline stores A, B, and C are 6, 2, and 3, respectively, the order of C, A, and B may be rearranged in the order of A, C, and B .

In this case, with respect to the first offline store having the same calculated final score, the first offline store having a shorter distance from the user's location may be determined as a priority.

According to an embodiment, the server 10 may further consider store business hours when searching for the first offline store. Specifically, the first offline stores may be sorted in the order in which the most business hours remain based on the current time.

The server 10 may provide the result (first search result) searched for in step S130 to the user terminal 20 (S150).

The user may check the necessary purchase product and the first offline store in which the purchase necessary product can be purchased through the user terminal 20 , visit a store located close to the user's location, and purchase the product. Through this, the user can experience the value directly through offline shopping and purchase the product directly without having to search for the product through recommendation for the product that needs to be purchased, so that the user can do offline shopping more efficiently.

On the other hand, the present invention may further include the step of providing, to the user terminal 20, display information of the product required to be purchased in a first offline store selected by the user terminal 20 among the searched first offline stores. .

Specifically, the server 10 receives a selection of a specific first offline store from the user terminal 20 from among the searched first offline stores, and transmits the location information on which the product to be purchased is displayed in the selected specific first offline store to the user terminal 20 ), so that the user can visit the first offline store and immediately find a product that needs to be purchased.

In this case, the display information may be provided in advance by the server 10 from the first offline store terminal 30 .

In addition, the present invention may further include the step of receiving, from the user terminal 20, an expected visit time for a first offline store selected by the user terminal 20 among the searched first offline stores.

Specifically, a user who has been recommended to the first offline store for offline purchase of a product that needs to be purchased may set a scheduled visit time of the first offline store and immediately receive the product to be purchased when visiting the store. For example, the first offline store linked to the personalized recommendation service of the present invention may deliver the product in a drive-through manner after pre-packaging the product according to the user's visit reservation. To this end, the user may perform a payment in advance for a product that needs to be purchased on the service platform.

According to an embodiment, the server 10 may provide a wear alarm service to the user. When there is no stock of the product required to be purchased in the offline store desired by the user other than the first offline store searched by the server 10 , the user may apply for a stock alarm service for the product required to be purchased. Thereafter, when a product that needs to be purchased is received in the corresponding offline store, a wear alarm is provided in the form of a message to the user terminal 20 , and at this time, the user inputs the scheduled visit time for the corresponding offline store through the user terminal 20 . When you visit the store, you can receive the products you need to purchase right away.

Referring to FIG. 3 , in the method of providing a personalized recommendation service for offline purchase performed by the server 10 according to an embodiment of the present invention, characteristic information on the owned clothing is analyzed based on the owned clothing information. step (S210), specifying the user in a multi-dimensional space indicating the degree of similarity between users based on the analysis result (S220), information on clothing owned in a user group located in a space adjacent to the user in the multi-dimensional space Based on the step (S230) of recommending a product (hereinafter, a recommended product) that matches the product required to be purchased, the step of searching for at least one second offline store for the recommended product (S240) and the second search result The step of providing to the user terminal (S250) may be further included. Hereinafter, detailed description of each step is described.

The server 10 may analyze the characteristic information on the owned clothes based on the user's own clothes information obtained in step S110 ( S210 ).

Specifically, referring to FIG. 4 , the server 10 extracts at least one clothing factor representing an attribute of clothing from the owned clothing information (S212), and adds the information to the owned clothing information based on the at least one clothing factor. By analyzing the characteristic information about the user, it is possible to calculate the characteristic value of the user (S214).

Here, the clothing factor may refer to information indicating an attribute of clothing or a clothing item related to clothing. For example, the type, color, shape, and shape (image), material, fabric pattern, price, size, brand name, and the like of owned clothing may be used as the clothing factor.

As an embodiment, the server 10 may extract at least one clothing factor representing the clothing attribute from the user's own clothing information. In addition, the server 10 may analyze characteristic information of the user's own clothing information based on the extracted at least one clothing factor, and may calculate the characteristic value of the user.

For example, the server 10 extracts the type, color, shape and shape (image) and material of clothes from the clothes A owned by the user as a clothing factor, and the type, color, and type of clothes from the clothes B owned by the user. When the shape and shape (image), price, and brand name are extracted as clothing factors, based on 4 clothing factors for owned clothing A and 5 clothing factors for owned clothing B, By analyzing the characteristic information, it is possible to calculate a characteristic value thereof.

Here, the characteristic information is information that can be grasped by analyzing all the clothes owned by each user, and preference information indicating preference information for clothes, a preferred style for clothes, and what types of clothes or clothing items they like. It may be information that can represent, etc.

In addition, the characteristic value may be expressed as a single value representing characteristic information about the user's own clothing, or may be expressed as a multidimensional value in the form of a vector or matrix instead of a single value. If the characteristic value is expressed as a multi-dimensional value based on the clothing factor, the multi-dimensional characteristic values for each user may be gathered and arranged in a high-order multi-dimensional space. The multidimensional space will be described later.

That is, the server 10 obtains the user's clothing information from each user and analyzes the characteristic information to determine the taste and preferred style of each user.

In the present invention, the server 100 may use social data in analyzing characteristic information of the user's own clothing information according to an embodiment.

The server 10 may acquire social data through the user terminal 20 . In this case, the social data may include various information that can be obtained from social media (eg, social network service (SNS), blog, Twitter, etc.) and users.

For example, a post posted by a user through social media, a comment on another's post, reaction information (eg, like click information, etc.) may be acquired as social data. In addition, other users (eg, acquaintances of the user) related to the user may acquire posts posted through social media, comments on other people's posts, reaction information (eg, like click information, etc.) as social data. can In addition, various information related to clothing and clothing items posted through social media may be acquired as social data. In addition, although not directly related to clothing, weather information that may have an indirect effect may be acquired as social data.

In addition, the social data may include horoscope feedback data for a result coordinated by each individual user or satisfaction horoscope data for recommended coordination. In an embodiment, the server 10 may obtain social data associated with the user and analyze characteristic information of the user's own clothing information by reflecting the obtained social data associated with the user.

That is, the server 10 may perform data mining on the social data together with the user's clothing information obtained in step S110 to analyze characteristic information on the user's own clothing information.

As described above, by using the user's own clothing information as well as social data associated with the user, the user's taste can be analyzed more precisely. In addition, by mining various social data, the user's taste that reflects the latest trends can be considered.

The server 10 may specify the user on the multidimensional space indicating the degree of similarity between users based on the result analyzed in step S210 (S220).

Specifically, referring to FIG. 5 , the server 10 may obtain coordinate information corresponding to the characteristic value in the multidimensional space (S222), and specify the user in the obtained coordinate information (S224).

Here, the multidimensional space is a space for indicating the degree of similarity between users, and it means a multidimensional coordinate space in which users having similar characteristic information can be mapped to the same or similar space based on the characteristic information of each user. can

In one embodiment, the server 10 may obtain coordinate information corresponding to the user's characteristic information (ie, characteristic value) in a multidimensional space, and may map the user to the obtained coordinate information.

Referring to FIG. 6 , the server 10 may map each user to a specific location in the multi-dimensional space 300 and 400 using characteristic information analyzed based on the clothing information possessed by each user. Each point shown on the multi-dimensional space 300 and 400 of FIG. 6 represents each user mapped to a specific location on the multi-dimensional space 300 and 400 .

At this time, the server 10 is configured to perform at least one user group 310 and 410 based on the degree of similarity between users mapped to a specific location on the multidimensional space 300 and 400 (ie, characteristic information such as taste and preference style). can create

In an embodiment, the server 10 may analyze characteristic information of each of the at least one user based on the clothing information possessed by the at least one user obtained from the at least one user terminal 20 . The server 10 may calculate coordinate information in the multidimensional space 300 and 400 corresponding to the characteristic information for each of at least one user, respectively, and map each user to the calculated coordinate information. The server 10 generates at least one user group 310 and 410 by grouping users showing similar characteristic information based on the characteristic information of each of at least one user mapped on the multidimensional space 300 and 400 . can do.

That is, it may be understood that users within the same user group 310 and 410 have the same or similar characteristic information (eg, taste, preference style, etc.). For example, users within the same user group 310 and 410 may own the same or similar clothing or clothing items.

In creating a user group in a multidimensional space, the server 10 applies artificial intelligence (i.e., data learning-based machine learning, deep learning, etc.) technology such as clustering and collaborative filtering. Users with characteristics can be grouped.

In addition, in creating a user group in the multidimensional space, the server 10 may map users on the multidimensional space 300 in a parametric model-based form, and may be graph-based. Users may be mapped on the multidimensional space 400 in the form.

The parametric model-based form or the graph-based form is merely mentioned as an example, and the embodiment of the present invention is not limited to the exemplified form and may be mapped or configured in various forms.

As shown in FIG. 6 , the server 10 may pre-build a multidimensional space in which users having the same or similar characteristic information are grouped and classified for a plurality of users. Thereafter, when the server 10 acquires owned clothing information from a new user or acquires new owned clothing information from an existing user, the server 10 analyzes the characteristic information and maps the corresponding user to a specific location in a pre-built multi-dimensional space. , it is possible to group the corresponding user into a user group having the same or similar characteristic information in the multidimensional space based on the mapped location.

In one embodiment, when the server 10 acquires new owned clothing information from the user terminal 20 of the existing user, the server 10 reflects the new owned clothing information together with the existing owned clothing information, thereby providing a characteristic of the corresponding user's own clothing information. Information can be updated. In this case, the server 10 may calculate coordinate information corresponding to the updated characteristic information in a multidimensional space based on the updated characteristic information, and change the location of the corresponding user with the calculated coordinate information. In this case, as the location of the user is changed in the multidimensional space, the user may be grouped into a user group other than the existing user group. In this case, it can be inferred that the user's characteristic (ie, taste or preferred style, etc.) is changed.

In addition, the server 10 acquires the user's clothing information from the user terminal 20 at regular intervals (eg, daily, weekly, monthly, etc.), and reflects the user's own clothing information acquired at regular intervals based on this Accordingly, the characteristic information of the user's own clothing information may be updated. In addition, the server 10 may periodically reflect the change in fashion taste by changing the location of the user in the multidimensional space based on the characteristic information updated at regular intervals.

Based on the result of specifying the user on the multi-dimensional space in step S220, the server 10 determines a product (hereinafter, recommended product) can be recommended (S230).

In one embodiment, the server 10 detects a user group located in a space adjacent to the user from among at least one user group generated in the multidimensional space, and based on the information on clothing owned by users in the detected user group, the purchase is necessary Clothing similar to a product may be extracted, and clothing coordinated with the extracted similar clothing by users in the detected user group may be acquired. Then, the server 10 may select the obtained clothing as the recommended product and recommend it to the user.

In another embodiment, the server 10 detects a user group located in a space adjacent to the user from among at least one user group created in the multidimensional space, and based on the information on clothing owned by users in the detected user group, the purchase is necessary Clothing similar to the product is extracted, and the extracted similar clothing from the high-star coordination based on the horoscope feedback data or satisfaction horoscope data for the coordination matched with the extracted similar clothing given by users within the detected user group. You can get clothes that were coordinated with you. Then, the server 10 may select the obtained clothing as the recommended product and recommend it to the user.

In this case, the detected user group may be the same user group as the user, or may be an adjacent user group adjacent to the user's group.

Each user group created in the multidimensional space is a group consisting of users having the same or similar characteristic information. Accordingly, there is a high probability that users within the same user group or user groups adjacent to each other have the same or similar clothing or clothing items. Accordingly, the server 10 may use clothes owned by other users in a distance adjacent to the user in the multidimensional space to increase the probability of recommending clothes (coordinates) that the user currently needs to purchase.

In the above description, the server 10 recommends one recommended product, but the present invention is not limited thereto, and a plurality of recommended products may be recommended.

The server 10 may search for at least one second offline store for the recommended product recommended in step S230 (S240).

According to an embodiment, the server 10 may sort the at least one second offline store according to the order in which the distance from the first offline store is close to each other, based on the distance from the first offline store.

In this case, the server 10 may extract an offline store having the recommended product as a second offline store among numerous offline stores linked to the personalized recommendation service. That is, the server 10 does not extract the offline store that does not have the recommended product as the second offline store.

The extracted second offline stores are sorted and searched in order of distance from the location of the selected first offline store, respectively.

For example, the second offline stores A, B, and C all have the recommended product A. The second offline store A is 0.5 km away from the location of the first offline store, and the second offline store B is If the distance from the location of the first offline store is 2 km and the distance from the location of the first offline store to the second offline store C is 5 km, the server 10 performs the calculated distance in the order of the shortest, that is, the second offline store. Stores can be sorted in the order of A, B, and C.

According to an embodiment, when the server 10 recommends a plurality of recommended products in step S230 , the server 10 selects the at least one second offline store based on the distance from the first offline store. The at least one second off-line store arranged in an order in which the distance to the offline store is close to each other, and based on the stock information for each of the plurality of recommended products, in an order in which the distance to the first offline store is close to the first offline store may be rearranged according to the order in which the number of recommended products in stock is greater among the plurality of recommended products.

In this case, the server 10 may extract, as the second offline store, an offline store having at least one recommended product among the plurality of recommended products among numerous offline stores linked to the personalized recommendation service. That is, the server 10 does not extract the offline store that does not have any of the plurality of recommended products as the second offline store.

The extracted second offline stores are sorted and searched in the order of distance from the location of the first offline store selected by the user, respectively. Specifically, scores may be given in the order of the closest distance to the calculated location of the first offline store, and the scores may be sorted in the order of the highest score.

For example, the second offline stores A, B, and C all have at least one of the plurality of recommended products. The second offline store A is 0.5 km away from the location of the first offline store, and the second If the offline store B has a distance of 2 km from the location of the first offline store and the second offline store C has a distance of 5 km from the location of the first offline store, the server 10 sends the second offline store with the shortest calculated distance. 3 points are given to store A, 2 points are given to the second offline store B with the second calculated distance, and 1 point is given to the second offline store C with the calculated distance farthest, and the score given It can be arranged in the order of A, B, and C of the second offline store having a high value.

In addition, the three second offline stores arranged based on the distance are rearranged according to the order in which the number of recommended products in stock among the plurality of recommended products is large. Specifically, the final score is calculated by giving weights according to the number of recommended products in stock to the scores given to each of the second offline stores A, B, and C, and the calculated final scores can be rearranged in the highest order. .

That is, if five recommended products (A, B, C, D, E) are recommended, the second offline store A holds only the stock for the recommended product A among the five recommended products, and the second offline store B If all of the recommended products A, B, C, D, and E are in stock among the 5 recommended products, and the second offline store C has stock of the recommended products A, B, and C among the 5 recommended products , since the number of recommended products in stock of each of the second offline stores A, B, and C is 1, 5, and 3, weights may be assigned based thereon.

For example, the second offline store A is 0.5 km away from the location of the first offline store, the second offline store B is 2 km away from the first offline store, and the second offline store C is the second offline store. If the distance from the location of the 1 offline store is 5 km, the second offline store is arranged in the order of A, B, and C, and 3 points, 2 points, and 1 point are given to each of the second offline stores A, B, and C; In the second offline store A, the number of recommended products in stock is 1, in the second offline store B, the number of recommended products in stock is 5, and in the second offline store C, the number of recommended products in stock If is 3, a weight of 3 points is given to the second offline store B having the largest number of recommended products in stock, and 2 points are given to the second offline store C having the second largest number of recommended products in stock A weight of 1 point may be assigned to the second offline store A having the smallest number of recommended products in stock. Accordingly, since the calculated final scores of the second offline stores A, B, and C are 3, 6, and 2, respectively, the order of A, B, and C may be rearranged in the order of B, A, and C .

In this case, with respect to the second offline store having the same calculated final score, the second offline store having a shorter distance from the location of the first offline store may be determined as a priority.

According to an embodiment, the server 10 may further consider the store business hours when searching for the second offline store. Specifically, the first offline stores may be sorted in the order in which the most business hours remain based on the current time.

In the above description, when searching for the second offline store, sorting has been made based on the distance from the location of the first offline store. It can be searched by sorting based on the distance.

The server 10 may provide the result (second result) searched for in step S240 to the user terminal (S250).

The user checks the recommended product and the second offline store where the recommended product can be purchased through the user terminal 20, purchases the product required to be purchased at the first offline store, and also at the second offline store close to the first offline store You can visit and purchase recommended products. Through this, the user can purchase products that need to be purchased and purchase products that are suitable for the product (personally customized coordination recommended by the service platform) while minimizing movement, enabling more efficient offline shopping.

According to the present invention, the first offline store for purchasing the necessary purchase product and the second offline store for purchasing the recommended product may be different, but may be the same. That is, the user may purchase the necessary purchase product and the recommended product in the adjacent first offline store and the second offline store, respectively, but when the first offline store and the second offline store are the same, the required purchase product and the recommended product in one offline store can also be purchased together.

Meanwhile, the present invention may further include providing display information of the recommended product of a second offline store selected by the user terminal 20 among the searched second offline stores to the user terminal 20 .

Specifically, the server 10 receives a selection of a specific second offline store from the user terminal 20 from among the searched second offline stores, and transmits location information on which the recommended product is displayed in the selected specific second offline store to the user terminal 20 . , so that the user can visit the second offline store and immediately find a recommended product.

In this case, the display information may be provided in advance by the server 10 from the second offline store terminal 30 .

In addition, the present invention may further include the step of receiving, from the user terminal 20, an expected visit time for a second offline store selected by the user terminal 20 among the searched second offline stores.

Specifically, a user who has been recommended to the second offline store for offline purchase of the recommended product may set a scheduled visit time of the second offline store and immediately receive the product to be purchased when visiting the store. For example, the second offline store linked to the personalized recommendation service of the present invention may deliver the product in a drive-through manner after pre-packaging the product according to the user's visit reservation. To this end, the user may perform payment for the recommended product in advance on the service platform.

According to an embodiment, the server 10 may provide a wear alarm service to the user. When there is no stock of the recommended product in the offline store desired by the user other than the second offline store searched for by the server 10 , the user may apply for an alarm service for wearing the recommended product. Thereafter, when a recommended product is received in the offline store, a wear alarm is provided to the user terminal 20 in the form of a message, and at this time, the user enters the scheduled visit time for the offline store through the user terminal 20. When you visit the store, you will be able to receive the recommended products you plan to purchase right away.

On the other hand, the present invention provides the step of determining whether there is clothing similar to the recommended product among the owned clothing of the user, and when it is determined that there is similar owned clothing, a coordination score between the similar owned clothing and the purchased product is provided. It may further include the step of

The server 10 determines whether there is clothing similar to the recommended product recommended in step S230 among the user's own clothing using the learning model constructed by using the owned clothing information obtained from each user as big data. can do.

If it is determined that there is no similar holding clothing, the server 10 may search the second offline store for the recommended product and provide it to the user terminal 20 (steps S240 and S250).

On the other hand, if it is determined that there is similar owned clothing, the server 10 may provide the user terminal 20 with a coordination score matching the recommended product and similar owned clothing with the purchase necessary product.

In this case, the coordination score may be a score calculated based on horoscope feedback data or satisfaction horoscope data input from other users for the coordination in which the recommended product and the purchase necessary product are matched.

If the user has a high coordination score that matches his/her own clothing similar to the recommended product with the product that needs to be purchased, the intention to purchase the product that needs to be purchased may increase, and after purchasing the product that needs to be purchased, holding similar to the recommended product You can also try it on by coordinating it with clothing.

In addition, the present invention may further include: obtaining image information of a desired product to be purchased from the user terminal; and providing a coordination recommendation to the user terminal based on the owned clothing information and image information of the desired product to be purchased. may be

Specifically, when a user visits an offline store without a plan and finds clothing that he or she likes, the corresponding clothing (the desired product to be purchased) is photographed through the user terminal 20 and provided to the server 10, the server 10 ) may provide a coordinating recommendation service by extracting owned clothing matching the desired product to be purchased from among the user's own clothing based on the user's clothing information.

Accordingly, the user can determine in advance whether there are clothes matching the desired product among the clothing owned by the user, and by wearing the desired product according to the recommended coordination, satisfaction with the purchase can be increased.

Alternatively, the user shows the employee of the offline store through the user terminal 20 the information on the clothing (image of the user registered in the application) of the clothing, and the employee recommends a product that matches the clothing in the store. you can also see

Alternatively, the user may show the coordinator recommended by the service platform through the user terminal 20 to the employee of the offline store, and determine the purchase of a product matching the recommended coordinator by referring to the employee's opinion on the recommended coordinating coordinator.

As described above, the present invention provides the value of direct experience through offline shopping and provides various information on the purchase experience of the buyer in order to solve the pain point of offline shopping that a lot of time is consumed until purchasing a product. By collecting and analyzing, it is possible to present and recommend necessary products to buyers, and also to present offline store information where they can purchase the product.

In addition, the user visits the offline store to try on and purchase the coordinated clothes, or finds or recommends matching clothes at the store, calculates the coordination score to see if it matches the clothes already owned, and judges the fit in advance to reduce the time of wearing. By minimizing the lengthy process and improving the offline purchasing experience, it can improve the purchase satisfaction of consumers and increase the repeat visit rate.

2 to 5 are described as sequentially executing steps S110 to S224, but this is merely illustrative of the technical idea of this embodiment, and those of ordinary skill in the art to which this embodiment belongs 2 to 5 in a range that does not deviate from the essential characteristics of the present embodiment, or to execute one or more steps of steps S110 to S224 in parallel in various modifications and variations to be applicable. 2 to 5 are not limited to a chronological order.

The method according to an embodiment of the present invention described above may be implemented as a program (or application) to be executed in combination with a server, which is hardware, and stored in a medium.

7 is a diagram schematically illustrating a configuration of an apparatus 500 for performing a method for providing a personalized recommendation service for offline purchase according to the present invention.

Referring to FIG. 7 , the processor 510 includes one or more cores (not shown) and a graphic processing unit (not shown) and/or a connection path (eg, a bus) for transmitting and receiving signals with other components. ) may be included.

The processor 510 according to an embodiment executes one or more instructions stored in the memory 520 to perform the method of providing a personalized recommendation service for offline purchase described in relation to FIGS. 1 to 6 .

The processor 510 executes one or more instructions stored in the memory 520 to obtain, from the user terminal, information on owned clothing related to the user's owned clothing from the user terminal, and use of the owned clothing based on the owned clothing information. extracting information, extracting a product required to be purchased from among the owned clothing based on the use information, searching for at least one first offline store for the product required to be purchased, and applying a first search result to the user terminal You can perform the steps provided by

The processor 510 executes one or more instructions stored in the memory 520 to analyze the characteristic information on the owned clothing based on the owned clothing information; specifying the user in a space; recommending a product (hereinafter referred to as a recommended product) suitable for the product to be purchased based on clothing information owned in a user group located in a space adjacent to the user in the multidimensional space; Searching for at least one second offline store for a recommended product and providing a second search result to the user terminal may be performed.

Meanwhile, the processor 510 includes a random access memory (RAM) and a read-only memory (ROM) that temporarily and/or permanently store signals (or data) processed inside the processor 510 . , not shown) may be further included. In addition, the processor 510 may be implemented in the form of a system on chip (SoC) including at least one of a graphic processing unit, a RAM, and a ROM.

The memory 520 may store programs (one or more instructions) for processing and controlling the processor 510 . Programs stored in the memory 520 may be divided into a plurality of modules according to functions.

The method for providing a personalized recommendation service for offline purchase according to an embodiment of the present invention described above may be implemented as a program (or application) and stored in a medium to be executed in combination with a computer, which is hardware.

The above-described program is C, C++, JAVA, machine language, etc. that a processor (CPU) of the computer can read through a device interface of the computer in order for the computer to read the program and execute the methods implemented as a program It may include code (Code) coded in the computer language of Such code may include functional code related to functions defining functions necessary for executing the methods, etc. can do. In addition, the code may further include additional information necessary for the processor of the computer to execute the functions or code related to memory reference for which location (address address) in the internal or external memory of the computer should be referenced. have. In addition, when the processor of the computer needs to communicate with any other computer or server located remotely in order to execute the functions, the code uses the communication module of the computer to determine how to communicate with any other computer or server remotely. It may further include a communication-related code for whether to communicate and what information or media to transmit and receive during communication.

The storage medium is not a medium that stores data for a short moment, such as a register, a cache, a memory, etc., but a medium that stores data semi-permanently and can be read by a device. Specifically, examples of the storage medium include, but are not limited to, ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage device. That is, the program may be stored in various recording media on various servers that the computer can access, or in various recording media on the computer of the measurement person. In addition, the medium may be distributed in a computer system connected by a network, and a computer readable code may be stored in a distributed manner.

The steps of a method or algorithm described in relation to an embodiment of the present invention may be implemented directly in hardware, as a software module executed by hardware, or by a combination thereof. A software module may contain random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any type of computer-readable recording medium well known in the art to which the present invention pertains.

As mentioned above, although embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art to which the present invention pertains can realize that the present invention can be embodied in other specific forms without changing its technical spirit or essential features. you will be able to understand Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

1: system
10: Server
20: user terminal
30: offline store terminal
40: communication network
500: device
510: processor
520: memory

Claims (10)

A method performed by a server, comprising:
obtaining, from the user terminal, information on owned clothing related to the user's own clothing;
extracting usage information on the owned clothes based on the owned clothes information;
extracting a product that needs to be purchased from among the owned clothing based on the usage information;
searching for at least one first offline store for the purchase necessary product; and
Including; providing a first search result to the user terminal;
The usage information includes the number of times of wearing the possession clothes and the date of purchase,
How to provide a personalized recommendation service for offline purchases. According to claim 1,
The first offline store search step includes:
based on the distance from the user's current location, sorting the at least one first offline store according to the order in which the distance from the user is closest;
When extracting the necessary purchase product, if a plurality of purchase necessary products are extracted,
The first offline store search step includes:
based on the distance from the user's current location, sorting the at least one first offline store according to the order in which the distance from the user is closest;
On the basis of the stock information for each of the plurality of purchase necessary products, the at least one first offline store arranged according to the order of the closest distance to the user is purchased from among the plurality of purchase necessary products in stock. to rearrange according to the order of the largest number of
How to provide a personalized recommendation service for offline purchases. According to claim 1,
Providing, to the user terminal, display information of the product required to be purchased in a first offline store selected by the user terminal among the searched first offline stores to the user terminal;
How to provide a personalized recommendation service for offline purchases. According to claim 1,
The method further comprising: receiving an expected visit time for a first offline store selected by the user terminal among the searched first offline stores from the user terminal;
How to provide a personalized recommendation service for offline purchases. According to claim 1,
analyzing characteristic information on the owned clothing based on the owned clothing information;
specifying the user in a multidimensional space representing a degree of similarity between users based on the analysis result;
recommending a product (hereinafter referred to as a recommended product) suitable for the product to be purchased based on clothing information owned by a user group located in a space adjacent to the user in the multidimensional space;
searching for at least one second offline store for the recommended product; and
Providing a second search result to the user terminal; further comprising,
How to provide a personalized recommendation service for offline purchases. 6. The method of claim 5,
The characteristic information analysis step is,
extracting at least one clothing factor representing an attribute of clothing from the owned clothing information; and
Calculating the characteristic value of the user by analyzing characteristic information on the owned clothing information based on the at least one clothing factor;
The user specific step is
obtaining coordinate information corresponding to the characteristic value in the multidimensional space; and
Including; specifying the user in the obtained coordinate information;
How to provide a personalized recommendation service for offline purchases. 6. The method of claim 5,
The second offline store search step includes:
based on the distance from the first offline store, sorting the at least one second offline store according to the order of the distance to the first offline store,
When recommending the above recommended products, if multiple recommended products are recommended,
The second offline store search step includes:
based on the distance from the first offline store, sorting the at least one second offline store according to the order of the distance to the first offline store,
Based on the inventory information for each of the plurality of recommended products, the at least one second offline store, which is arranged according to the order of the distance from the first offline store, is a recommended product in stock among the plurality of recommended products. to rearrange according to the order of the largest number of
How to provide a personalized recommendation service for offline purchases. 6. The method of claim 5,
determining whether there is owned clothing similar to the recommended product among the owned clothing of the user; and
When it is determined that there is similar owned clothing, providing a coordination score between the similar owned clothing and the product required to be purchased; further comprising:
How to provide a personalized recommendation service for offline purchases. According to claim 1,
obtaining image information of a desired product to be purchased from the user terminal; and
Further comprising; based on the information on the clothes in possession and the image information of the desired product to be purchased, providing a coordination recommendation to the user terminal;
How to provide a personalized recommendation service for offline purchases. a memory storing one or more instructions; and
a processor that executes the one or more instructions stored in the memory;
The processor, by executing the one or more instructions,
obtaining, from the user terminal, information on owned clothing related to the user's own clothing;
extracting usage information on the owned clothes based on the owned clothes information;
extracting a product to be purchased from among the owned clothing based on the usage information;
searching for at least one first offline store for the purchase necessary product; and
performing a; providing a first search result to the user terminal;
Device.

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