WO2020141799A2 - Procédé pour fournir un service de recommandation d'article de mode à un utilisateur au moyen de données de calendrier - Google Patents

Procédé pour fournir un service de recommandation d'article de mode à un utilisateur au moyen de données de calendrier Download PDF

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WO2020141799A2
WO2020141799A2 PCT/KR2019/018492 KR2019018492W WO2020141799A2 WO 2020141799 A2 WO2020141799 A2 WO 2020141799A2 KR 2019018492 W KR2019018492 W KR 2019018492W WO 2020141799 A2 WO2020141799 A2 WO 2020141799A2
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user
item
image
fashion
style
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Ceased
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English (en)
Korean (ko)
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WO2020141799A3 (fr
Inventor
유애리
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Odd Concepts Inc
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Odd Concepts Inc
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Recommending goods or services
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
    • G06F16/5854—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content using shape and object relationship
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0252—Targeted advertisements based on events or environment, e.g. weather or festivals
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions

Definitions

  • the present invention relates to a method of providing a fashion item recommendation service to a user, and more particularly, to a method of providing a fashion item recommendation service using calendar data obtained from a user device.
  • the following is to provide a fashion item recommendation service using calendar data obtained from a user device from a user device.
  • a method of providing a fashion item recommendation service to a user using a service server comprises: obtaining user images from an image capturing module; Automatically obtaining calendar data associated with the user images through a user device; Generating a user fashion database by associating the calendar data with fashion items extracted from the user image; Receiving a recommendation service request, extracting a specific fashion item associated with the calendar data corresponding to an event associated with the recommendation service from the user fashion database; Retrieving the same category recommendation item similar to the specific fashion item extracted based on the image similarity from a product database configured by indexing a label extracted from the content of the product; Retrieving an item similar to the specific fashion item based on image similarity, from a style database consisting of style images from which style labels representing human feelings are represented by computer-recognizable data; Determining an item of a different category from the similar item as a coordination item from the style image in which the similar item is searched; Retrieving, from the product database,
  • the calendar data includes weather information related to at least one of temperature, humidity, UV index, wind, fine dust, and ozone amount, based on time data and location data included in the user images, and the user images
  • weather information related to at least one of temperature, humidity, UV index, wind, fine dust, and ozone amount, based on time data and location data included in the user images, and the user images
  • the user images In connection with the time data, it may be characterized in that it comprises the event information of the user obtained through the schedule management program installed on the user device.
  • the step of generating the user fashion database may include updating the user fashion database using calendar data inputted from the user.
  • the image capturing module is controlled through the user device,
  • the image taking module may be characterized by including a coupling portion detachably coupled to a part of the external member.
  • a link to the user is performed. It may be characterized by adding a connection point.
  • a style label represented by computer-recognizable data is extracted from at least one fashion item included in the style image, and the style image is clustered and generated based on the extracted style label. It may be characterized by consisting of at least one style book (style book) that shares the style label of.
  • FIG. 1 is a reference diagram for explaining a system for providing a fashion item recommendation service using user calendar data according to an embodiment of the present invention.
  • FIG. 2 is a reference diagram for explaining a method of providing a fashion item recommendation service to a user using calendar data according to an embodiment of the present invention.
  • the user device on which the product information is displayed is a mobile device, but the present invention is not limited thereto. That is, in the present invention, the user device should be understood as a concept including all types of electronic devices capable of requesting search and displaying advertisement information, such as a desktop, a smart phone, and a tablet PC.
  • the term displayed on a user device refers to a screen loaded on an electronic device and/or content inside the screen so that it can be immediately displayed on the screen according to the user's scroll. It can be understood as an inclusive concept.
  • an entire execution screen of an application that is extended in a horizontal or vertical direction and displayed according to a user's scroll may be included in the concept of the page, and the screen being rolled by the camera may also be included in the concept of the page.
  • the recommendation service providing system may include an image capturing module 110, a user device 120, and a service server 130.
  • the image photographing module 110 includes a camera, a camcorder, and the like capable of generating an identifiable digital image by photographing a subject, and may be combined with a human body sensor.
  • the location data such as time data such as the date, time, and day of the image creation and the GPS coordinates in which the image is generated may be included in the image and stored.
  • the image capturing module 110 may include a coupling part (e.g. cradle, magnetic holder, etc.) to be detachably attached to an external member (e.g. mirror).
  • the image generated by the image photographing module may include a certain pattern, for example, a room background, which may help to perform more effective pre-processing in the image processing process.
  • the image pre-processing process includes, for example, a process for grasping the overall fit of the user by distinguishing the user's head and feet as the subject from the image captured by the image capture module. It can contain.
  • the user device 120 may store a user's schedule, images, and the like, and use a smart phone, a smart pad, and the like that can transmit and receive data to and from other devices, such as an image capture module and a service server, through a wired or wireless network. Includes.
  • the user device 120 stores the user's schedule (eg meeting, travel..., etc.) information or weather information application (eg K-weather, Naver weather..., etc.), schedule management application (eg Google Calendar, Naver Calendar... Etc.).
  • schedule eg meeting, travel..., etc.
  • weather information application eg K-weather, Naver weather..., etc.
  • schedule management application eg Google Calendar, Naver Calendar... Etc.
  • the image capturing module and the user device are separately described, but the present invention can be applied even when the image capturing module and the user device are combined, such as a smart phone.
  • the service server 130 provides a fashion item recommendation service to the user based on information related to the user image received from the image photographing module 110 and the user device 120, schedule information of the user, and the like.
  • FIG. 2 is a reference diagram for explaining a method of providing a fashion item recommendation service to a user using calendar data according to an embodiment of the present invention.
  • the service server in order to extract a fashion item using calendar data, the service server first acquires user images from the image capturing module (S210).
  • the image capture module may generate a user image.
  • the user image may be generated at regular time intervals or periods. For example, when a user looks at clothes worn by a user through a mirror, the image photographing module may detect a human body and photograph clothes coordinated by the user. In this case, the image capturing module may pre-process only the image related to the user's clothing (for example, the section from the user's head to the foot) on the captured image.
  • the service server automatically or manually acquires calendar data associated with user images through the user device (S220).
  • the calendar data may include information related to weather, schedules, or user activities corresponding to time data and location data related to the user image.
  • the calendar data may include temperature, humidity, UV index, wind, fine dust or ozone amount information based on time data and location data included in user images. If the user took a photo at 20 o'clock on December 25, 2018, the image was created at 20 o'clock on December 25, 2018, and the temperature in Myeong-dong on December 25, 2018 was minus 2 degrees, humidity was 52%, and the UV index was Weather information such as 25%, wind 3m/s in the northwest, bad fine dust, and low ozone can be defined as calendar data.
  • the weather information may be obtained through the weather information application on the user device based on the time data and the location data of the user image, but has only the time and location data of the user image, and weather information corresponding to it on the server or other device May be obtained.
  • the user's schedule which may be included in the calendar data, and data related to the user's activity may be automatically obtained through a schedule management program installed on the user device in association with the user's time data, or may be manually input from the user. Since the user's schedule is generally different for each individual user, unlike the weather information described above, it is preferable that it is acquired on the user device or added/modified according to the user's input. Therefore, if the schedule management program (for example, Google Calendar, etc.) has a schedule called'date' all day on December 25, 2018, or if the user randomly enters'date with XXX', an event called'date' It can be included in the calendar data.
  • a schedule management program for example, Google Calendar, etc.
  • the service server extracts fashion items from user images received from the image photographing module, and generates a user fashion database by associating calendar data related to the user image obtained through the user device (S230). For example, among images stored in the user device, images that can be identified by the user are image-processed, and the fashion items worn by the user are extracted and sorted based on the user's face, and then the fashion items are sorted.
  • a user fashion database can be generated by associating calendar data.
  • the user fashion database may be updated.
  • a user named A wears a white bag and a brown suit exists on the user device, and the photo is associated with calendar data where an event called'meeting' exists, based on the user's face A
  • The'white bag' and the'brown suit' can be stored in the user fashion database along with the associates'meeting'. Accordingly, when a user requests a fashion recommendation service for an event called'conference', the service server searches for fashion items associated with the event'conference' among style databases and user images learned using a fashion magazine or the like. And based on this, a recommended product can be provided.
  • a fashion item for the product image may be added to the user fashion database.
  • a user named A may capture an image from an online page of a shopping mall where a bag is purchased and add it to the user's fashion database.
  • the image of the site is added to the user's fashion database. It can be.
  • the user fashion database may include information on fashion items, such as a size of a fashion item, a label expressing a feeling that a person feels in a fashion item as computer-recognizable data, and a picture when the user fits.
  • the user fashion database may include necessary size information such as a user's top, bottom, and dress, and the appearance when fitting the actual clothes is managed as a photograph, so that the user fits considering his body type You can make this possible.
  • the user may refer to the future when considering fit when selecting a fashion item.
  • the image information when the user is fitted or information that can estimate the user's preferences, such as the user's purchase data and viewing time data, the user's size information, the preferred price point for online shopping for fashion items, use , May contain information about the brand.
  • the user fashion database may include user identification information, user behavior information for estimating the user size, user size estimated from the behavior information, and user size information directly received from the user device.
  • the service server provides a query to the user device for the user's age, gender, occupation, fashion field of interest, reserved items, etc., receives user input for the query, generates user size information, and generates the user fashion. It can be reflected in the database.
  • the service server is a time when a user browses an arbitrary style book provided through an application according to an embodiment of the present invention, item information generated like a tag, request item, fashion item information purchased through the application or another application, and By combining user behavior information for estimating the user size, such as time information at which the information is generated, preference information for a style in which the user is interested in the corresponding time may be generated and reflected in the user fashion database.
  • the service server may generate the user's body shape information and reflect it in the user fashion database.
  • the service server models a user's body model from a machine learning framework that learns human body features from a large number of body images. You can create The user body model may include information about the proportions and skin tones of each part of the user's body as well as the size information of each part of the user's body.
  • the service server may generate preference information for a user's fashion item and reflect it in the user fashion database.
  • the preference information may include information about a user's preferred price, preferred brand, and preferred use. For example, when a user browses or purchases a fashion item through an online market on a user device, the service server reflects different weights for viewing or purchasing to generate information about a preferred price, a preferred brand, and a preferred use, and the user fashion database. Can be reflected in.
  • the service server has a feature of estimating a user's “flavor” corresponding to a human feeling, and generating the estimated taste information in a form recognizable by a computer and reflecting it in a user fashion database.
  • the service server may extract a label for estimating the user's taste from the user's behavior information.
  • the label may be extracted as the meaning of fashion items included in user behavior information, such as a style book viewed by a user, an item that generates a tag like a tag, a request item, or a purchase item.
  • the label may be generated as information about a look and feel, such as the appearance and feel of fashion items included in user behavior information, and trends.
  • the label generated from the user behavior information is weighted according to the user behavior, and the service server may generate user size information for estimating the user size by combining it and store it in the user fashion database.
  • the user size information, user body shape information, and user preference information included in the user fashion database may be used to set the exposure priority for the recommended item or the recommended product.
  • the specific fashion item associated with the calendar data corresponding to the event associated with the recommendation service is extracted from the user fashion database (S240). That is, when a request for a fashion item recommendation service related to an event called'meeting' is received as described above, the fashion items associated with the calendar data called'meeting' among user images are searched, and specific fashion items are extracted based on this. Can. If weather information is used, it is possible to search for fashion items worn by a user in connection with '5 degrees of image and 20% humidity', and extract specific fashion items from them.
  • the service server When the service server receives a recommendation service request for a specific fashion item using calendar data, the service server provides at least one recommendation product included in the same category or a different category using the product database and the style database (S250).
  • a photo of a specific fashion item is extracted, and a recommendation item of the same fashion category can be provided, or another category item can be recommended/provided as a coordination item that goes well with it.
  • the style database may include information about a fashion image, a fashion image that can be referenced for coordination of a plurality of items, among images collected on the web.
  • the style database may include, among images collected online, images combined with a plurality of fashion items to match well (referred to herein as a style image) and classification information for the style image.
  • the style image according to the embodiment of the present invention is a fashion catalog, a fashion magazine pictorial image, a fashion show shooting image, an idol costume image, a specific drama that can be collected on the web as image data generated by combining a plurality of fashion items in advance by an expert or an expert Or, you can exemplify a movie's costume image, SNS, blog celebrity's costume image, fashion magazine's street fashion image, or an image coordinated with other items to sell fashion items.
  • the style image is stored in a style database according to an embodiment of the present invention, and can be used to determine other items that go well with a particular item.
  • the style image can be used as a reference for a computer to understand the human feeling that it is generally “fits well”. Machine learning learned about matching of multiple fashion items in order for the computer to recommend another item that “matches well” without human intervention for any item, because “being good” with any item is about the human feeling. You will need a framework.
  • the service server may collect a plurality of fashion items combined by an expert or a semi-expert and collect a style image worn by a person and generate it as a style database.
  • the service server can train the framework by applying the style database to the machine learning framework.
  • a machine learning framework that has learned a large number of style images with matching blue shirts and brown ties could recommend brown ties as a coordination item for requests for blue shirts.
  • the service server may collect style images online.
  • the service server collects a list of web addresses such as fashion magazines, fashion brands, drama makers, celebrity planners, SNS, online stores, etc., and checks the website to track the link. Can be collected.
  • the style database may include a fashion item extracted from the user images described above.
  • a point for material compensation for a user related to the user image may be set. This is defined as a link point in the present invention, and can be used to compensate a user in various forms such as points and mileage.
  • the service server can collect and index images from websites such as fashion magazines, fashion brands, drama makers, celebrity planners, SNS, online stores, etc. Information may be provided separately.
  • the service server may filter images that are not suitable for style recommendation among the collected images.
  • the service server may filter the remaining images, leaving only the images containing the human-shaped objects among the collected images and the plurality of fashion items.
  • Filtering images for a single fashion item is appropriate because the style image is used to determine the request item and other items that can be coordinated. Furthermore, constructing a database with images of a person wearing a plurality of fashion items directly may be more useful than images of the fashion items themselves. Therefore, the service server according to an embodiment of the present invention may determine the style image included in the style database by filtering the remaining images, leaving only the image including the human-shaped object and the plurality of fashion items.
  • the service server may process features of the fashion item object image included in the style image. More specifically, the service server may extract image features of the fashion item object included in the style image, express feature information as vector values, generate feature values of the fashion item object, and structure feature information of the images.
  • the service server may extract a style label from a style image and cluster style images based on the style label. It is appropriate that the style label is extracted with respect to the look and feel of the fashion item's appearance, feel, and trends. According to a preferred embodiment of the present invention, it is possible to extract a label for a feeling that a person can feel from the appearance of a single fashion item included in a style image, a combination of a plurality of items, and use it as a style label. For example, a celebrity look, a magazine look, a summer look, a feminine look, a sexy look, an office look, a drama look, and a Chanel look can be illustrated as style labels.
  • the service server defines a style label in advance, generates a neural network model learning the characteristics of the image corresponding to the style label, classifies objects in the style image, and extracts the label for the object can do.
  • the service server may assign the corresponding label to the image matching the specific pattern with a random probability through the neural network model learning the pattern of the image corresponding to each label.
  • the service server may learn characteristics of an image corresponding to each style label to form an initial neural network model, and apply a large number of style image objects to it to expand the neural network model more precisely. have.
  • the service server may apply style images to a neural network model formed of a hierarchical structure formed of a plurality of layers without separate learning of labels. Furthermore, weighting is applied to the feature information of the style image according to the request of the corresponding layer, and the product images are clustered using the processed feature information, and the celebrity look, magazine look, summer look, feminine look, and sexy look to the clustered image group , Labels that are interpreted ex post as office look, drama look, or chanel look can be given.
  • the service server may cluster style images using a style label and generate a plurality of style books. This is to be provided as a reference to the user.
  • the user may browse a specific style book among a plurality of style books provided by the service server, find a favorite item, and request a product information search for the corresponding item.
  • the service server may pre-classify items having a very high appearance rate, such as white shirts, jeans, and black skirts.
  • items having a very high appearance rate such as white shirts, jeans, and black skirts.
  • jeans are very basic items in fashion, so the appearance rate in style images is very high. Therefore, no matter what item the user inquires about, the probability of matching jeans as a coordination item will be significantly higher than other items.
  • the service server can pre-classify an item having a very high appearance rate in a style image as a buzz item, and generate a style book with different versions, including a buzz item and a buzz item. have.
  • buzz items may be classified by reflecting time information. For example, considering the fashion cycle of a fashion item, it is possible to consider items that fad and disappear for a month or two, fashion items that return each season, and items that are continuously fashionable for a certain period of time. Therefore, by reflecting time information in the classification of the buzz item, if a specific fashion item has a very high appearance rate during an arbitrary period, the item may be classified as a buzz item along with information on the corresponding period.
  • the buzz item is classified as described above, in the subsequent item recommendation step, there is an effect that can be recommended in consideration of whether the item to be recommended is fashionable or unrelated to fashion.
  • the service server can process a specific fashion item object included in the received request, and search a style database based on image similarity. That is, the service server may search for a similar item in the style database by processing an image object specified as a search target.
  • the service server may extract characteristics of the image object to be searched and structure specific information of images for efficiency of search.
  • the service server can extract label and/or category information on the meaning of the object image to be searched by applying the machine learning technique used to construct the product image database to the processed object image to be processed.
  • the label may be expressed as an abstracted value, but may also be expressed in text form by interpreting the abstracted value.
  • the service server may extract labels for women, dresses, sleeveless, linen, white, and casual look from the request object image.
  • the service server may use labels for women and dresses as category information of the requested object image, and labels for sleeveless, linen, white, and casual look may be used as label information describing characteristics of the object image outside the category. .
  • the service server may search the style database based on the similarity of the request object image. This is for retrieving items similar to the request image from the style database to identify other items matching the similar items in the style image. For example, the service server may display the request object image and the fashion item object images included in the style image. The similarity of the feature values can be calculated, and an item whose similarity is within a preset range can be checked.
  • the service server processes the feature values of the request image by reflecting the weights required by the multiple layers of the artificial neural network model for machine learning configured for the product database, and within a certain range with the request image At least one fashion item group included in a style book having a distance value may be selected, and items belonging to the group may be determined as similar items.
  • the service server determines a similar item by searching the style database based on the similarity of the requested image, and uses label and category information extracted from the image to increase the accuracy of the image search. Can.
  • the service server calculates the similarity between the feature values of the requested image and the style database image, and among the products having a similarity of a predetermined range or higher, the label and/or category information does not match the label and/or category information of the requested image. Similar items can be determined by excluding products.
  • the service server may calculate the item similarity only in the style book having label and/or category information matching the label and/or category information of the request image.
  • the service server may extract a style label from a request image, and specify a similar item based on the request and image similarity in a style book matching the label.
  • the service server may specify a similar item based on the similarity of the requested image and the image in the style database without extracting a separate label from the requested image.
  • the service server may extract a label of tropical from the request. Thereafter, the service server may identify a similar item having a similarity of a leaf pattern dress and a preset range in a style book clustered with a label of tropical.
  • the service server includes a similar item retrieved from the style book, and may provide a user device with a style image in which the similar item is combined with other fashion items.
  • a style image in which straw hats, rattan bags, etc. are combined with a leaf pattern dress can be provided to the user.
  • the service server may determine a coordination item by combining the similar items with the similar items to identify fashion items of other categories included in the style image.
  • a specific fashion item inquired by the user may be searched based on the image similarity in the style database, and a fashion item of another category matched with the similar item in the style image including the similar item may be considered as a recommended item. This is because the service server according to the embodiment of the present invention is learned to match other items that match with the requested item in the style image.
  • the service server may determine a product similar to the coordination item from the product database as a recommended product.
  • the product database may include product detail information such as origin, size, sales place, and wear shot of products sold in the online market, and is characterized by composing product information based on the image of the product.
  • the service server may collect product information for products sold in an arbitrary online market as well as product information of an online market affiliated in advance.
  • the service server may include a crawler, a parser, and an indexer, collect web documents of online stores, and access text information such as product images, product names, and prices included in web documents.
  • a crawler may collect a list of web addresses of an online store, check a website, and track links to deliver data related to product information to a service server.
  • the parser analyzes web documents collected during the crawling process to extract product information such as product images, product prices, and product names included in the page, and the indexer can index the corresponding location and meaning.
  • the service server may collect and index product information from a website of any online store, but may receive product information in a preset format from an affiliate market.
  • the service server can process the product image. This is for determining a recommended item based on whether the product image is similar, without relying on text information such as a product name or a sales category.
  • a recommended item may be determined based on whether the product image is similar, but the present invention is not limited thereto. That is, depending on the implementation, the product image as well as the product name or sales category may be used as a single or secondary request.
  • the service server may generate a database by structuring text information such as a product name and product category other than the product image.
  • the service server can extract the features of the product image and index the feature information of the images for efficiency of search.
  • the service server may detect feature areas of product images (Interest Point Detection).
  • the feature region refers to a main region for extracting a descriptor for a feature of an image, that is, a feature descriptor, to determine whether the images are identical or similar.
  • such a feature area may be a contour that an image includes, a corner such as a corner among contours, a blob separated from the surrounding area, an area that is invariant or covariant according to image deformation, or ambient brightness. It can be a pole with dark or light features, and it can be a patch (fragment) of the image or the entire image.
  • the service server may extract feature descriptors from the feature area.
  • the feature descriptor is a vector value representing features of an image.
  • such a feature descriptor can be calculated using the location of the feature region for the corresponding image, or the brightness, color, sharpness, gradient, scale, or pattern information of the feature region.
  • the feature descriptor may calculate the brightness value of the feature region, the change value of the brightness, or the distribution value by converting it to a vector.
  • the feature descriptor for the image is a local descriptor based on the feature area as described above, as well as a global descriptor, a frequency descriptor, a frequency descriptor, a binary descriptor, or the like. It can be expressed as a neural network descriptor.
  • the feature descriptor is a global descriptor that converts and extracts the brightness, color, sharpness, gradient, scale, pattern information, etc. of each image or a region where the image is divided by an arbitrary reference, or each feature region into a vector value ( Global descriptor).
  • the feature descriptor is a frequency descriptor (Frequency Descriptor) that converts and extracts the number of specific descriptors previously included in the image, the number of inclusions of global features such as a previously defined color table, and the like into a vector value.
  • Binary descriptor which is extracted and used as an integer after extracting in bit units whether the size of each element constituting or including the descriptor is larger or smaller than a specific value, learns from the layer of the neural network Or, it may include a neural network descriptor (Neural Network descriptor) for extracting the image information used for classification.
  • Neural Network descriptor Neural Network descriptor
  • the feature information vector extracted from the product image it is possible to convert the feature information vector extracted from the product image to a lower dimension.
  • the feature information extracted through the artificial neural network corresponds to 40,000-dimensional high-dimensional vector information, and it is appropriate to convert to a low-dimensional vector in an appropriate range in consideration of resources required for search.
  • Various feature reduction algorithms such as PCA and ZCA can be used to transform the feature information vector, and feature information converted into a low dimensional vector can be indexed into a corresponding product image.
  • the service server can extract a label for the meaning of the image by applying a machine learning technique based on the product image.
  • the label may be expressed as an abstracted value, but may also be expressed in text form by interpreting the abstracted value.
  • the service server may define a label in advance, generate a neural network model learning the characteristics of the image corresponding to the label, classify objects in the product image, and extract labels for the corresponding object.
  • the service server may assign the corresponding label to the image matching the specific pattern with a random probability through the neural network model learning the pattern of the image corresponding to each label.
  • the service server may learn characteristics of an image corresponding to each label to form an initial neural network model, and apply a large amount of product image objects to it to expand the neural network model more precisely. Furthermore, the service server may create a new group including the product if the product is not included in any group.
  • the service server predefines a label that can be used as meta information about the product such as female bottoms, skirts, dresses, short sleeves, long sleeves, patterns, materials, colors, and abstract feelings (innocent, chic, vintage, etc.) ,
  • a neural network model learning the characteristics of the image corresponding to the label may be generated, and the neural network model may be applied to the advertiser's product image to extract a label for the product image to be advertised.
  • the service server may apply product images to a neural network model formed of a hierarchical structure formed of a plurality of layers without separate learning of labels. Furthermore, the feature information of the product image may be weighted according to the request of the corresponding layer, and the product images may be clustered using the processed feature information.
  • additional analysis may be necessary to determine whether the corresponding images are clustered according to which attribute of the feature value, that is, to connect the clustering results of the images with a concept that can be recognized by a real human.
  • the service server classifies products into three groups through image processing, and extracts the labels A for characteristics of the first group, B for characteristics of the second group, and C for characteristics of the third group. It needs to be interpreted ex postly, that A, B, and C, respectively, refer to female tops, blouses, and plaids, respectively.
  • the service server gives the clustered image group a label that can be interpreted ex post with female bottoms, skirts, dresses, short sleeves, long sleeves, patterns, materials, colors, and abstract feelings (pure, chic, vintage, etc.) , Labels assigned to an image group to which an individual product image belongs may be extracted as a label of the corresponding product image.
  • the service server may express a label extracted from a product image as text, and a text-type label may be used as tag information of the product.
  • the tag information of the product is directly and subjectively given by the seller, resulting in inaccuracy and poor reliability.
  • the product tag subjectively assigned by the seller has a problem of lowering the efficiency of search by acting as noise.
  • the tag information of the product is based on the image of the corresponding product. Since it can be extracted mathematically without human intervention, it has the effect of increasing the reliability of the tag information and improving the accuracy of the search.
  • the service server may generate category information of the corresponding product based on the content of the product image. For example, if a label for an arbitrary product image is extracted as a woman, top, blouse, linen, stripe, long sleeve, blue, office look, the service server sets the label for a woman, top, or blouse as the category information of the product. Labels for linen, stripe, long-sleeved, blue, and office look can be used as label information describing the characteristics of products outside the category. Alternatively, the service server may index the corresponding product without distinguishing the label and category information. At this time, the category information and/or label of the product may be used as a parameter to increase the reliability of the image search.
  • the service server determines an item similar to the coordination item from the product database configured by indexing the label extracted from the content of the above-mentioned product as a recommended item, and provides product information for the recommended item, similar to the recommended item You can search for products in the product database.
  • the service server may search the product database based on the image similarity for the coordination item determined using the style database.
  • the service server can extract characteristics of the coordination item object and structure specific information of images for efficiency of search.
  • the service server may search the product database based on the similarity of the object image. For example, the service server may calculate the similarity between the feature values of the recommended item image and the product image included in the product database, and determine a product whose similarity is within a preset range as a recommended product.
  • the service server processes the feature values of the recommended item image by reflecting the weights required by multiple layers of the artificial neural network model for machine learning configured for the product database, and the distance within a certain range At least one product group having a value may be selected, and products belonging to the group may be determined as recommended products.
  • the service server may specify the recommended product based on the label extracted from the recommended item object.
  • the service server searches the object image and the object image to be searched only for product groups that have the female top as category information in the product database. Similarity can be calculated.
  • the service server may set products having a degree of similarity or higher than a predetermined range as a candidate candidate for recommendation, and may exclude products whose sub-category information is not a blouse from the recommended candidate.
  • products with sub-category information indexed as blouses may be selected as advertisement items.
  • the service server in the product database has a woman top, blouse, long sleeve, lace, and collar neck as a label. It is also possible to calculate the similarity between the recommended item and the image for the group only.
  • the service server may determine the priority of exposure by reflecting user preference/size information. For example, when the user is interested in the office look, the weight of the office look label may be used to calculate the priority and provide recommended product information according to the calculated priority.
  • the service for recommending a fashion item to a user using the calendar service as described above can be applied to various services.

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Abstract

La présente invention concerne un procédé pour fournir un service de recommandation d'article de mode à un utilisateur à l'aide d'un serveur de service. Spécifiquement, la présente invention est caractérisée en ce qu'elle comprend : une étape consistant à acquérir des images d'utilisateur à partir d'un module de capture d'image; une étape consistant à acquérir automatiquement des données de calendrier associées aux images d'utilisateur par l'intermédiaire d'un dispositif d'utilisateur; une étape de génération d'une base de données de mode d'utilisateur par association des données de calendrier avec des articles de mode extraits des images d'utilisateur; une étape d'extraction d'un article de mode spécifique, associé aux données de calendrier correspondant à un événement associé à un service de recommandation, à partir de la base de données de mode d'utilisateur lors de la réception d'une demande de service de recommandation; une étape de recherche dans une base de données de produits, configurée par indexation d'étiquettes extraites à partir de détails de produit, pour trouver des articles recommandés qui sont dans la même catégorie et similaires à l'article de mode spécifique extrait sur la base d'une similarité d'image; une étape de recherche dans une base de données de style, composée d'images de style dans lesquelles des étiquettes de style représentant les sentiments d'une personne en tant que données reconnaissables par ordinateur ont été extraites de la base de données de style, pour trouver des articles qui sont similaires à un article de mode spécifique sur la base d'une similarité d'image; une étape de détermination d'articles dans une catégorie différente de celle des articles similaires, parmi les images de style à partir desquelles les articles similaires ont été recherchés, en tant qu'articles à coordonner; une étape de recherche dans la base de données de produits pour trouver des articles candidats qui sont dans une catégorie différente et similaires aux articles à coordonner sur la base de la similarité d'image; et une étape consistant à fournir un article candidat dans la même catégorie et/ou un article candidat dans une catégorie différente en tant que produit recommandé.
PCT/KR2019/018492 2019-01-03 2019-12-26 Procédé pour fournir un service de recommandation d'article de mode à un utilisateur au moyen de données de calendrier Ceased WO2020141799A2 (fr)

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KR1020190000763A KR102271090B1 (ko) 2019-01-03 2019-01-03 사용자에게 캘린더 데이터를 이용하여 패션 아이템 추천 서비스를 제공하는 방법

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CN114936317A (zh) * 2022-04-29 2022-08-23 上海花千树信息科技有限公司 一种基于图像内容分析的婚恋交友推荐方法及装置

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