KR20170096971A - Method for recommending a product using style feature - Google Patents
Method for recommending a product using style feature Download PDFInfo
- Publication number
- KR20170096971A KR20170096971A KR1020170021441A KR20170021441A KR20170096971A KR 20170096971 A KR20170096971 A KR 20170096971A KR 1020170021441 A KR1020170021441 A KR 1020170021441A KR 20170021441 A KR20170021441 A KR 20170021441A KR 20170096971 A KR20170096971 A KR 20170096971A
- Authority
- KR
- South Korea
- Prior art keywords
- style
- image
- product
- query
- learning data
- Prior art date
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06F16/53—Querying
- G06F16/532—Query formulation, e.g. graphical querying
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06Q30/0255—Targeted advertisements based on user history
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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—Item recommendations
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- General Physics & Mathematics (AREA)
- Development Economics (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Economics (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Software Systems (AREA)
- Image Analysis (AREA)
Abstract
A method for recommending a product using style features, comprising: acquiring a query image from a user client; extracting a category and a style feature of the query image; using a learned model using a plurality of learning data images; Searching for at least one product image having style characteristics similar to the query image from the product image database, wherein the retrieved product image belongs to a category different from the category of the query image, and retrieving the retrieved at least one product image And providing the user data to the user client, wherein the plurality of learning data images include label information indicating a category and a style characteristic of each of the plurality of learning data images.
Description
A product recommendation method using a style feature is disclosed. More particularly, the present invention relates to a product recommendation method and system for recognizing a product from a query image input by a user and recommending a product image of another category matching the recognized product to a user.
Fashion styling is important for people who are involved in social activities. Because fashion styling is the basis of instant judgment.
As the importance and importance of fashion styling has increased, fashion styling service through the Internet has been provided. The fashion styling service through the Internet is performed in such a manner that, when a user registers product information on an Internet site, the stylists directly select products suitable for the registered product and present them to the user.
However, this method is problematic in that it takes a lot of time and money to select a product because the product selection is performed by stylists.
In addition, the existing recommendation engine based on machine learning is predominantly based on the machine learning method based on user's purchase history and user's preference. This method has a problem in that it takes a lot of time and money to collect enough user data because the screening of goods is performed based on the user data.
SUMMARY OF THE INVENTION It is an object of the present invention to provide a product recommendation method using a style feature.
The problems to be solved by the present invention are not limited to the above-mentioned problems, and other problems which are not mentioned can be clearly understood by those skilled in the art from the following description.
According to an aspect of the present invention, there is provided a product recommendation method using style features, including: obtaining a query image from a user client; extracting a category and a style feature of the query image; A search method for searching at least one product image having style characteristics similar to the query image from a product image database using a model learned using a plurality of learning data images, And providing the searched at least one merchandise image to the user client, wherein the plurality of learning data images comprise at least one of the plurality of learning data images, .
In addition, the learned model may include a feature extraction unit and an image search unit, and the step of extracting the category and style features of the query image may include extracting category and style characteristics of the query image using the feature extraction unit of the learned model, And the step of searching for the at least one goods image may include searching for the at least one goods image using the image searching unit of the learned model.
In addition, the step of retrieving the at least one merchandise image may include receiving from the user client a selection input for at least one style contained in a style feature of the query image, and receiving at least one merchandise corresponding to the selected style And searching for an image.
The learned model may be a model that associates the plurality of learning data images with a style feature space representing a relationship between the plurality of learning data images. May be used to extract the style characteristics of each of the plurality of learning data images included in the style minutiae space and the distance between the plurality of learning data images may be determined according to the extracted style characteristics.
The style minutiae space may be characterized in that learning data images having similar style characteristics among the plurality of learning data images are arranged close to each other and learning data images having different style characteristics are arranged far away from each other, The step of searching for one merchandise image may include determining a reference merchandise image similar to the query image within the style minutiae space and searching at least one merchandise image located within a predetermined distance from the determined reference merchandise image Step < / RTI >
The plurality of learning data images may further include relationship information indicating a connection relationship between the plurality of learning data images.
The learned model may be a model that associates the plurality of learning data images with one or more style feature point spaces classified based on style features of the plurality of learning data images, Space is used to extract a style feature of each of a plurality of learning data images included in each of the one or more style feature point spaces, The distance between the plurality of learning data images can be determined.
The step of retrieving the at least one article image may further include the steps of determining a reference article image similar to the query image in a style feature point space corresponding to at least one style included in a style feature of the query image, And searching for at least one merchandise image located within a predetermined distance from the determined reference merchandise image within the space.
In addition, the learned model may include a generative model for generating a target product image having a style characteristic similar to the query image, and the step of searching for the at least one product image may include: Generating at least one target product image having a style characteristic similar to the query image using the target model and retrieving at least one product image similar to the at least one target product image from the product image database can do.
In addition, the step of generating the target product image may include generating a target product image corresponding to at least one of a style feature and a category corresponding to the selection input received from the user client.
Other specific details of the invention are included in the detailed description and drawings.
It is possible to recognize a product from a query image input by a user and automatically recommend a product of another category having a style characteristic similar to the recognized product so that it is possible to reduce the time and cost required for recommending the product to the user.
In addition, since the product is automatically recommended to the user by using the stylistic feature point extracted through the learning of the product image database from the query image input by the user, product recommendation is possible without the help of user data.
The effects of the present invention are not limited to the above-mentioned effects, and other effects not mentioned can be clearly understood by those skilled in the art from the following description.
1 is a diagram illustrating a method of recommending a product according to an embodiment.
2 is a diagram illustrating a method of searching for a recommended product matching a query product according to an embodiment.
3 is a diagram showing a method of recommending a product considering the style of a query product.
4 is a diagram showing an example of data for learning a model recommending a product using a style feature.
FIG. 5 is a diagram illustrating a style feature point space according to an exemplary embodiment.
6 is a view showing a style feature point space according to another embodiment.
7 is a diagram illustrating a method of using a generator model according to an embodiment.
FIG. 8 is a flowchart briefly illustrating a method for recommending a product using a style feature according to an embodiment.
BRIEF DESCRIPTION OF THE DRAWINGS The advantages and features of the present invention and the manner of achieving them will become apparent with reference to the embodiments described in detail below with reference to the accompanying drawings. It should be understood, however, that the invention is not limited to the disclosed embodiments, but may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, Is provided to fully convey the scope of the present invention to a technician, and the present invention is only defined by the scope of the claims.
The terminology used herein is for the purpose of illustrating embodiments and is not intended to be limiting of the present invention. In the present specification, the singular form includes plural forms unless otherwise specified in the specification. The terms " comprises "and / or" comprising "used in the specification do not exclude the presence or addition of one or more other elements in addition to the stated element. Like reference numerals refer to like elements throughout the specification and "and / or" include each and every combination of one or more of the elements mentioned. Although "first "," second "and the like are used to describe various components, it is needless to say that these components are not limited by these terms. These terms are used only to distinguish one component from another. Therefore, it goes without saying that the first component mentioned below may be the second component within the technical scope of the present invention.
Unless defined otherwise, all terms (including technical and scientific terms) used herein may be used in a sense that is commonly understood by one of ordinary skill in the art to which this invention belongs. In addition, commonly used predefined terms are not ideally or excessively interpreted unless explicitly defined otherwise.
Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
1 is a diagram illustrating a method of recommending a product according to an embodiment.
Referring to FIG. 1, an example is shown in which a
The
The
The
In this specification, the goods represented by the
The
In this specification, 'category' means the type of clothing. For example, categories include tops, bottoms, shoes, bags, hats, and the like.
As used herein, 'style' refers to the style of clothing commonly used in the industry. For example, a style may include casual and office styles, but the manner in which styles are classified is not limited. Also, the style may be classified into a large category and a small category belonging to each large category. For example, casual styles can be subdivided into business casual and young casual.
In one embodiment, the
In addition, the
However, unlike the category classification, the classification of the style can be relatively unclear. For example, jeans and even casual style can be matched, but it can also match the style of the office. Also, in recent years, there are many business casual style clothes that are difficult to clearly classify office and casual styles, so it may be difficult to clearly classify the style as the category.
Therefore, the
For example, a query item can be both casual style and office style if it is judged that the probability of a query item being a casual style is 60% and the probability of an office style being 40%. Thus, the style feature may be information that includes information that the query item is both casual and office style, but a little closer to the casual style.
The
In addition, the
Accordingly, the
In another embodiment, the query item may belong to both a casual style and an office style. In this case, the
In another embodiment, the query item may have a 60% chance of belonging to a casual style and a 40% chance of belonging to an office style. In this case, the
In addition, the
Accordingly, the
2 is a diagram illustrating a method of searching for a recommended product matching a query product according to an embodiment.
In one embodiment, the
The
Deep learning is a set of machine learning algorithms that try to achieve a high level of abstraction (a task that summarizes key content or functions in large amounts of data or complex data) through a combination of several nonlinear transformation techniques. Is defined. Deep learning can be viewed as a field of machine learning that teaches computers how people think in a big way.
When there is any data, it is represented by the form that the computer understands (for example, the pixel information is represented by a column vector in the case of the image), and many researches How to create expression models and how to model them). As a result of these efforts, various deep-running techniques have been developed. Deep learning techniques include Deep Neural Networks (DNN), Convolutional Deep Neural Networks (CNN), Recurrent Neural Networks (RNN), and Deep Belief Networks (DBN). have. The
In addition, the
The
As learning data for learning the model, a learning image pair can be used. The learning image pair includes a first learning image pair and a second learning image pair.
The first learning image pair is made up of images of products matching each other. And the second learning image pair is composed of images of products that do not match each other.
In one embodiment, the first learning pair and the second learning pair may use a database obtained from an online shopping mall. For example, using the data obtained from the database of the online shopping mall, a list of goods purchased by consumers together can be obtained. The
In another embodiment, the first learning pair and the second learning pair use a predetermined algorithm for determining whether or not they match each other based on the shape, color, and pattern of each clothes, Data may be used.
In one embodiment, the first learning pair and the second learning pair may be comprised of images of goods belonging to different categories and matching or unrelated to each other.
According to one embodiment, the learning image pair may include images of two items. For example, the first learning image pair is composed of images including images matching shoes and shoes. As another example, the second learning image pair is composed of images including images that are not compatible with oral and verbal images.
According to another embodiment, the learning image pair may include images of n commodities. For example, the first learning image pair is composed of images including shoes, a tops that match shoes, trousers that match shoes, skirts that match shoes, and bags that match shoes. As another example, the second learning image pair may be composed of images including shoes, shoes that do not match with shoes, pants that do not match with shoes, skirts that do not match with shoes, bags that do not match shoes.
When the learning is completed using the learning image pair as described above, the images of the products matching each other among the images of the products belonging to different categories are arranged close to each other in the feature point space. For example, white casual shirts and black casual pants are different colors, but both products are casual style, so they can be seen as matching products. Thus, the white casual shirt and the black casual pants are placed close together within the feature space.
On the other hand, the images of the products that do not match each other among the images of the products belonging to different categories are located far away in the minutiae space. For example, a casual shirt and a suit pants can be seen as a commodity that do not match each other. Thus, the casual shirt and the suit pants are placed far away from each other within the feature space.
Referring to FIG. 2, a
The
The
The
The
3 is a diagram showing a method of recommending a product considering the style of a query product.
The
In one embodiment, when the query product included in the
When the query product included in the
In addition, when the query product included in the
In addition, the
Hereinafter, the product recommendation method using the style feature will be described in detail. In the disclosed embodiment, the
4 is a diagram showing an example of data for learning a model recommending a product using a style feature.
The
For example, the
Referring to Fig. 4, an example of data used for learning a model is shown. According to the disclosed embodiment, the data used to train the model includes at least one merchandise image.
In one example, the merchandise image may be uploaded by an administrator (not shown) of the
For example, the label information includes information on a category and a style of a product included in each product image.
In this specification, 'category' means the type of clothing. For example, categories include tops, bottoms, shoes, bags, hats, and the like.
As used herein, 'style' refers to the style of clothing commonly used in the industry. For example, a style may include casual and office styles, but the manner in which styles are classified is not limited. Also, the style may be classified into a large category and a small category belonging to each large category. For example, casual styles can be subdivided into business casual and young casual.
As shown in FIG. 4, each merchandise image includes style information such as a casual style or an office style. Further, each merchandise image includes category information such as a bag, shoes, or pants.
The
In one embodiment, the learning data used for the learning of the model further includes relationship information indicating a connection relationship between product images used for learning.
The connection relationship between the product images includes information on whether or not they fit together. For example, information as to whether or not the particular top and bottom are worn together is stored in the learning data in the form of information on the connection relationship between the specific top and bottom.
In the present embodiment, 'to match' is judged according to the conventional wisdom of a fashion person or a general consumer, and can mean aesthetically good effect when the two goods are worn together. If the specific top and bottom of the embodiment have similar style characteristics, then the bottom of the specific top can match. However, they may not match each other due to the specific color or shape of the specific image and the underneath. Accordingly, the learning data may further include information on category and style, as well as information on whether each product image matches with each other.
Relationship information indicating whether product images belonging to different categories match each other may be inputted by the manager or may be automatically generated by the
FIG. 5 is a diagram illustrating a style feature point space according to an exemplary embodiment.
According to the disclosed embodiment, the
5, the style
Within the
The
In one embodiment, the
According to the embodiment, in the style
The
6 is a view showing a style feature point space according to another embodiment.
According to the disclosed embodiment, the
In one embodiment, the
For example, the
The method for determining the style to which each product image belongs and specific values are provided for the sake of illustration. Actually, a method of determining the style to which each product image belongs is not limited.
The
Referring to FIG. 6, a first style
A method of generating the
Therefore, in the first style
The
The
The
According to the disclosed embodiment, the
7 is a diagram illustrating a method of using a generator model according to an embodiment.
In one embodiment, the
The generative model is used to create models that can generate images that are similar to real images from sample images. For example, a generative model can be used to generate human face images such as real photographs from human face images.
Likewise, the generator model can be used to generate an image matching the actual image from the learning data as shown in FIG. For example, a generative model can be used to create a costume image of a similar style to match the actual costume.
Referring to FIG. 7, a
When the
The
FIG. 8 is a flowchart briefly illustrating a method for recommending a product using a style feature according to an embodiment.
Referring to FIG. 8, a product recommendation method using a style feature is composed of steps that are processed in a time-series manner in the
In step S710, the
In step S720, the
In step S730, the
The plurality of learning data images include label information indicating a category and a style characteristic of each of the plurality of learning data images.
In step S740, the
The steps of a method or algorithm described in connection with the embodiments of the present invention may be embodied directly in hardware, in software modules executed in hardware, or in a combination of both. The software module may be a random access memory (RAM), a read only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a flash memory, a hard disk, a removable disk, a CD- May reside in any form of computer readable recording medium known in the art to which the invention pertains.
While the present invention has been described in connection with what is presently considered to be practical exemplary embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, You will understand. Therefore, it should be understood that the above-described embodiments are illustrative in all aspects and not restrictive.
10: query video
20 and 30: Featured Video
100: Server
200: User Client
Claims (10)
Obtaining a query image from a user client;
Extracting category and style characteristics of the query image;
A search method for searching at least one product image having style characteristics similar to the query image from a product image database using a model learned using a plurality of learning data images, , ≪ / RTI > And
Providing the retrieved at least one merchandise image to the user client; Lt; / RTI >
Wherein the plurality of learning data images comprise label information indicating a category and style characteristic of each of the plurality of learning data images.
Wherein the learned model includes a feature extraction unit and an image search unit,
Wherein the extracting of the category and style features of the query image comprises:
And extracting a category and a style feature of the query image using the feature extraction unit of the learned model,
The step of retrieving the at least one merchandise image comprises:
And retrieving the at least one merchandise image using an image retrieval unit of the learned model.
The step of retrieving the at least one merchandise image comprises:
Receiving a selection input for at least one style included in a style feature of the query image from the user client; And
Retrieving at least one merchandise image corresponding to the selected style; / RTI >
The learned model may include:
Wherein the plurality of learning data images correspond to a style feature space representing a relationship between the plurality of learning data images.
Wherein the style minutiae space is used to extract a style characteristic of each of the plurality of learning data images included in the style minutiae space and determines a distance between the plurality of learning data images according to the extracted style characteristic How.
The style minutiae space comprises:
Learning data images having similar style characteristics among the plurality of learning data images are arranged close to each other and learning data images having different style characteristics are arranged far away from each other,
The step of retrieving the at least one merchandise image comprises:
Determining, within the style feature point space, a reference product image similar to the query image; And
Searching at least one merchandise image located within a predetermined distance from the determined reference merchandise image; / RTI >
Wherein the plurality of learning data images include:
Further comprising relationship information indicating a connection relationship between the plurality of learning data images.
The learned model may include:
Wherein the plurality of learning data images correspond to at least one style minutiae space classified on the basis of style characteristics,
Wherein the one or more style feature point spaces are used to extract a style feature of each of a plurality of learning data images included in each of the one or more style feature point spaces, Wherein a distance between the plurality of learning data images is determined according to a connection relationship.
The step of retrieving the at least one merchandise image comprises:
Determining a reference merchandise image similar to the query image in a style feature point space corresponding to at least one style included in a style feature of the query image; And
Searching at least one merchandise image located within a predetermined distance from the determined reference merchandise image within the style minutiae space; / RTI >
The learned model may include:
And a generative model for generating a target product image having style characteristics similar to the query image,
The step of retrieving the at least one merchandise image comprises:
Generating at least one target product image having style characteristics similar to the query image using the generative model; And
Retrieving from the merchandise image database at least one merchandise image similar to the at least one target merchandise image; / RTI >
Wherein the step of generating the target product image comprises:
Generating a target product image corresponding to at least one of a style feature and a category corresponding to a selection input received from the user client.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
PCT/KR2017/001801 WO2017142361A1 (en) | 2016-02-17 | 2017-02-17 | Method for recommending product using style characteristic |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
KR20160018450 | 2016-02-17 | ||
KR1020160018450 | 2016-02-17 |
Related Child Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
KR1020190028948A Division KR20190029567A (en) | 2016-02-17 | 2019-03-13 | Method for recommending a product using style feature |
Publications (1)
Publication Number | Publication Date |
---|---|
KR20170096971A true KR20170096971A (en) | 2017-08-25 |
Family
ID=59761700
Family Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
KR1020170021441A KR20170096971A (en) | 2016-02-17 | 2017-02-17 | Method for recommending a product using style feature |
KR1020190028948A KR20190029567A (en) | 2016-02-17 | 2019-03-13 | Method for recommending a product using style feature |
Family Applications After (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
KR1020190028948A KR20190029567A (en) | 2016-02-17 | 2019-03-13 | Method for recommending a product using style feature |
Country Status (1)
Country | Link |
---|---|
KR (2) | KR20170096971A (en) |
Cited By (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
KR20190093813A (en) * | 2018-01-19 | 2019-08-12 | 네이버 주식회사 | Method and system for recommending product based on artificial intelligence |
KR20190109652A (en) * | 2018-03-07 | 2019-09-26 | 네이버 주식회사 | Method and system for recommending product based style space created using artificial intelligence |
WO2019182378A1 (en) * | 2018-03-21 | 2019-09-26 | Lg Electronics Inc. | Artificial intelligence server |
KR20190119219A (en) * | 2018-04-02 | 2019-10-22 | 카페24 주식회사 | Main image recommendation method and apparatus, and system |
JP2019207508A (en) * | 2018-05-28 | 2019-12-05 | 株式会社リコー | Image search apparatus, image search method, image search program, and product catalog generation system |
KR20200013141A (en) * | 2018-07-17 | 2020-02-06 | 주식회사 비주얼 | Method and electric apparatus for ordering jewelry product |
KR20200023705A (en) * | 2018-08-22 | 2020-03-06 | 주식회사 비주얼 | Method and electric apparatus for recommending jewelry product |
CN110909754A (en) * | 2018-09-14 | 2020-03-24 | 哈尔滨工业大学(深圳) | Attribute generation countermeasure network and matching clothing generation method based on same |
CN111179031A (en) * | 2019-12-23 | 2020-05-19 | 第四范式(北京)技术有限公司 | Training method, device and system for commodity recommendation model |
KR20200104607A (en) * | 2019-02-27 | 2020-09-04 | 주식회사 마크애니 | Personalized item recommendation method and apparatus using image analysis |
KR20210016593A (en) * | 2018-01-19 | 2021-02-16 | 네이버 주식회사 | Method and system for recommending product based on artificial intelligence |
WO2021215758A1 (en) * | 2020-04-23 | 2021-10-28 | 오드컨셉 주식회사 | Recommended item advertising method, apparatus, and computer program |
CN116127111A (en) * | 2023-01-03 | 2023-05-16 | 百度在线网络技术(北京)有限公司 | Picture searching method, picture searching device, electronic equipment and computer readable storage medium |
Families Citing this family (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
KR20200141373A (en) * | 2019-06-10 | 2020-12-18 | (주)사맛디 | System, method and program of constructing dataset for training appearance recognition model |
KR102150720B1 (en) | 2020-01-03 | 2020-09-02 | 주식회사 스타일쉐어 | Image embedding apparatus and method for content-based user clustering |
KR102133039B1 (en) * | 2020-03-30 | 2020-07-10 | 서명교 | Server for providing apparel shopping mall platform |
KR102178961B1 (en) | 2020-04-21 | 2020-11-13 | 주식회사 스타일쉐어 | Artificial neural network apparatus and method for recommending fashion item using user clustering |
KR102178962B1 (en) | 2020-04-21 | 2020-11-13 | 주식회사 스타일쉐어 | Creator recommendation artificail neural network apparatus and method for fashion brand |
KR102642704B1 (en) * | 2021-11-01 | 2024-03-04 | 소리달 주식회사 | Shoes recommending apparatus based stereo image |
KR102628994B1 (en) * | 2023-04-24 | 2024-01-25 | 주식회사 엔피오이 | AI-based personalized bag recommendation system for consumers |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
KR100511210B1 (en) | 2004-12-27 | 2005-08-30 | 주식회사지앤지커머스 | Method for converting 2d image into pseudo 3d image and user-adapted total coordination method in use artificial intelligence, and service besiness method thereof |
-
2017
- 2017-02-17 KR KR1020170021441A patent/KR20170096971A/en not_active Application Discontinuation
-
2019
- 2019-03-13 KR KR1020190028948A patent/KR20190029567A/en active Application Filing
Cited By (17)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
KR20210016593A (en) * | 2018-01-19 | 2021-02-16 | 네이버 주식회사 | Method and system for recommending product based on artificial intelligence |
KR20190093813A (en) * | 2018-01-19 | 2019-08-12 | 네이버 주식회사 | Method and system for recommending product based on artificial intelligence |
KR20190109652A (en) * | 2018-03-07 | 2019-09-26 | 네이버 주식회사 | Method and system for recommending product based style space created using artificial intelligence |
WO2019182378A1 (en) * | 2018-03-21 | 2019-09-26 | Lg Electronics Inc. | Artificial intelligence server |
US11531864B2 (en) | 2018-03-21 | 2022-12-20 | Lg Electronics Inc. | Artificial intelligence server |
KR20190119219A (en) * | 2018-04-02 | 2019-10-22 | 카페24 주식회사 | Main image recommendation method and apparatus, and system |
JP2019207508A (en) * | 2018-05-28 | 2019-12-05 | 株式会社リコー | Image search apparatus, image search method, image search program, and product catalog generation system |
US11900423B2 (en) | 2018-05-28 | 2024-02-13 | Ricoh Company, Ltd. | Image retrieval apparatus image retrieval method, product catalog generation system, and recording medium |
KR20200013141A (en) * | 2018-07-17 | 2020-02-06 | 주식회사 비주얼 | Method and electric apparatus for ordering jewelry product |
KR20200023705A (en) * | 2018-08-22 | 2020-03-06 | 주식회사 비주얼 | Method and electric apparatus for recommending jewelry product |
CN110909754A (en) * | 2018-09-14 | 2020-03-24 | 哈尔滨工业大学(深圳) | Attribute generation countermeasure network and matching clothing generation method based on same |
CN110909754B (en) * | 2018-09-14 | 2023-04-07 | 哈尔滨工业大学(深圳) | Attribute generation countermeasure network and matching clothing generation method based on same |
KR20200104607A (en) * | 2019-02-27 | 2020-09-04 | 주식회사 마크애니 | Personalized item recommendation method and apparatus using image analysis |
CN111179031A (en) * | 2019-12-23 | 2020-05-19 | 第四范式(北京)技术有限公司 | Training method, device and system for commodity recommendation model |
CN111179031B (en) * | 2019-12-23 | 2023-09-26 | 第四范式(北京)技术有限公司 | Training method, device and system for commodity recommendation model |
WO2021215758A1 (en) * | 2020-04-23 | 2021-10-28 | 오드컨셉 주식회사 | Recommended item advertising method, apparatus, and computer program |
CN116127111A (en) * | 2023-01-03 | 2023-05-16 | 百度在线网络技术(北京)有限公司 | Picture searching method, picture searching device, electronic equipment and computer readable storage medium |
Also Published As
Publication number | Publication date |
---|---|
KR20190029567A (en) | 2019-03-20 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
KR20190029567A (en) | Method for recommending a product using style feature | |
JP7196885B2 (en) | Search system, search method, and program | |
JP7272490B2 (en) | Search support system and search support method | |
CN104504055B (en) | The similar computational methods of commodity and commercial product recommending system based on image similarity | |
Jagadeesh et al. | Large scale visual recommendations from street fashion images | |
US20200320769A1 (en) | Method and system for predicting garment attributes using deep learning | |
KR102072339B1 (en) | Image feature data extraction and use | |
KR100687906B1 (en) | System for recommendation the goods and method therefor | |
US20220138831A1 (en) | Method of Providing Fashion Item Recommendation Service Using User's Body Type and Purchase History | |
WO2018228448A1 (en) | Method and apparatus for recommending matching clothing, electronic device and storage medium | |
KR102317432B1 (en) | Method, apparatus and program for fashion trend prediction based on integrated analysis of image and text | |
CN110110181A (en) | A kind of garment coordination recommended method based on user styles and scene preference | |
TW201411515A (en) | Interactive clothes searching in online stores | |
US20200134694A1 (en) | Automatic fashion outfit composition and recommendation system and method | |
US20150120759A1 (en) | System and method for item and item set matching | |
US9460342B1 (en) | Determining body measurements | |
KR20200045668A (en) | Method, apparatus and computer program for style recommendation | |
De Melo et al. | Content-based filtering enhanced by human visual attention applied to clothing recommendation | |
KR102295459B1 (en) | A method of providing a fashion item recommendation service to a user using a date | |
KR20200141251A (en) | Method of advertising personalized fashion item and server performing the same | |
KR20200042203A (en) | Outfit coordination system and method based on user input Images | |
CN114201681A (en) | Method and device for recommending clothes | |
KR102495868B1 (en) | Fashion-related customized perfume recommendation system using ai | |
KR102200038B1 (en) | A method of providing a fashion item recommendation service to a user using a date | |
KR20210131198A (en) | Method, apparatus and computer program for advertising recommended product |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
A201 | Request for examination | ||
E902 | Notification of reason for refusal | ||
E902 | Notification of reason for refusal | ||
E601 | Decision to refuse application | ||
A107 | Divisional application of patent | ||
WITB | Written withdrawal of application |