WO2020156306A1 - Procédé, système et dispositif de traitement d'informations de collocation de vêtements, et procédé, système et dispositif de traitement d'objets de données - Google Patents

Procédé, système et dispositif de traitement d'informations de collocation de vêtements, et procédé, système et dispositif de traitement d'objets de données Download PDF

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Publication number
WO2020156306A1
WO2020156306A1 PCT/CN2020/073140 CN2020073140W WO2020156306A1 WO 2020156306 A1 WO2020156306 A1 WO 2020156306A1 CN 2020073140 W CN2020073140 W CN 2020073140W WO 2020156306 A1 WO2020156306 A1 WO 2020156306A1
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data object
combination
type
rule
data
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PCT/CN2020/073140
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English (en)
Chinese (zh)
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曹阳
章人可
戴能
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阿里巴巴集团控股有限公司
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Publication of WO2020156306A1 publication Critical patent/WO2020156306A1/fr

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    • 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
    • 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
    • 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

Definitions

  • the present invention relates to the field of computer technology, and in particular to a processing method, data object processing method, system and equipment for clothing matching information.
  • the common logic for recommending products on e-commerce platforms is a recommendation technology based on user behavior.
  • Most of the results recommended to users are products that belong to the same category as the products that have been purchased, collected or viewed. For example, if users are interested in dresses, they often browse and click on dresses on the platform, and a variety of similar dresses will be displayed in the recommended traffic window. This method of recommendation will result in repeated recommendations. The user has clearly purchased the product, but the system repeatedly recommends similar products, which is likely to cause confusion for the user and a poor user experience.
  • the various embodiments of the present application provide a clothing matching information processing method, a data object processing method, a system, and a device that solve or partially solve the above problems.
  • a method for processing clothing matching information includes:
  • a data object combination that meets the matching rules is selected as a clothing matching example that can be referenced by the user.
  • a data object processing method includes:
  • the at least one second data object is matched with the first data object, the at least one second data object is provided to the user.
  • a method for processing clothing matching information includes:
  • the clothing matching instance is a combination of data objects that meet the matching rules selected from a combination of multiple data objects; the combination of multiple data objects is a combination of at least one type that is different from the type of the first data object. Is generated by combining the second data object extracted from the data object set with the first data object.
  • a method for processing clothing matching information includes:
  • the data object combination that meets the matching rule among the multiple data object combinations is fed back to the client as a clothing matching instance.
  • a system for processing clothing matching information includes:
  • the client is configured to send request information corresponding to the specified operation to the server in response to the specified operation performed by the user on the first data object; receive the clothing matching instance that the server feedbacks on the first data object; The clothing matching instance is provided to the user;
  • the server is used to obtain the request information sent by the client for the first data object; from a set of at least one type of data object that is different from the type of the first data object, extract the information used to communicate with the first data object
  • the combined second data object is used to generate a plurality of data object combinations; the data object combination that meets the matching rule among the plurality of data object combinations is fed back to the client as a clothing matching instance.
  • an electronic device includes a memory and a processor; among them,
  • the memory is used to store programs
  • the processor is coupled with the memory, and is configured to execute the program stored in the memory for:
  • a data object combination that meets the matching rules is selected as a clothing matching example that can be referenced by the user.
  • an electronic device in another embodiment, includes a memory and a processor; among them,
  • the memory is used to store programs
  • the processor is coupled with the memory, and is configured to execute the program stored in the memory for:
  • the at least one second data object is matched with the first data object, the at least one second data object is provided to the user.
  • a client device in an embodiment of the present application, includes a memory and a processor; wherein,
  • the memory is used to store programs
  • the processor is coupled with the memory, and is configured to execute the program stored in the memory for:
  • the clothing matching instance is a combination of data objects that meet the matching rules selected from a combination of multiple data objects; the combination of multiple data objects is a combination of at least one type that is different from the type of the first data object. Is generated by combining the second data object extracted from the data object set with the first data object.
  • a server device includes a memory and a processor; wherein,
  • the memory is used to store programs
  • the processor is coupled with the memory, and is configured to execute the program stored in the memory for:
  • the data object combination that meets the matching rule among the multiple data object combinations is fed back to the client as a clothing matching instance.
  • the technical solution provided by the embodiment of the present application extracts a second data object to be combined with the first data object from a data object set of at least one type different from the type of the first data object to generate multiple data object combinations, and then Use matching rules instead of strict matching templates to select clothing matching examples from multiple data object combinations that can be referenced by users.
  • the universality is strong, and the output is richer under the premise of ensuring matching quality
  • the technical solutions provided by the embodiments of this application can produce multiple sets of matching suggestions for 99% of the products in the target product pool, and the satisfaction rate of manual evaluation reaches 80%.
  • FIG. 1 is a schematic flowchart of a method for processing clothing matching information provided by an embodiment of the application
  • FIG. 2 is a diagram of data object circulation in which a method of processing clothing matching information is described in principle by taking a dress as an example in an embodiment of the application;
  • FIG. 3 is a schematic diagram of a combination rule provided by an embodiment of this application.
  • FIG. 4 is a table representing the style matching relationship according to an embodiment of the application.
  • FIG. 5 is a schematic flowchart of a data object processing method provided by another embodiment of the application.
  • FIG. 6 is a schematic structural diagram of a clothing matching information processing system provided by an embodiment of the application.
  • FIG. 7 is a schematic flowchart of a method for processing clothing matching information according to another embodiment of the application.
  • FIG. 8 is a schematic flowchart of a method for processing clothing matching information provided by another embodiment of this application.
  • FIG. 9 is a schematic structural diagram of an apparatus for processing clothing matching information provided by an embodiment of the application.
  • FIG. 10 is a schematic structural diagram of a data object processing device provided by another embodiment of the application.
  • FIG. 11 is a schematic structural diagram of a clothing matching information processing device provided by another embodiment of the application.
  • FIG. 12 is a schematic structural diagram of a clothing matching information processing device provided by another embodiment of the application.
  • FIG. 13 is a schematic structural diagram of an electronic device provided by an embodiment of the application.
  • the first one is based on manual editing
  • the quality is high, but the number of matching is very small.
  • the quality is very high, but due to human efficiency, the daily output is usually in the order of hundreds of thousands and the quantity is very small. Platform products are in the hundreds of millions, so a large number of products cannot be matched manually. Collocations that have nothing to do with the user's own purchased products will not work for the user.
  • Plans based on related purchases are of average quality and large in quantity.
  • a matching sample is formed, and the model is trained to automatically produce matching.
  • This method will encounter the problem of too much noise in the training samples, because usually the clothing products purchased by users within a period of time are not guaranteed to be purchased for the same wear, so the quality of the training samples is not high (or the samples are processed to match the matching quality The required cost is too high), resulting in limited model effects; this method is only suitable for related product recommendation.
  • the third type is based on templates
  • the scheme based on matching templates is of high quality, with a large quantity under the open product set and a small quantity under the limited product set.
  • manual matching is used as a template, and a single product is used to find similar functions.
  • One set of matching is expanded into multiple sets of similar matching. This method can effectively improve the quality of a certain combination. Number of matches.
  • the (manual) matching templates that the platform can accumulate are usually in the order of less than one million, which can meet the needs of platform products that can easily be tens of millions or even over 100 million (the number of combinations that can be arranged and combined is almost infinite)
  • the products and the matching forms that can be covered are still limited.
  • a matching template has high requirements for the richness of the product.
  • a 4-piece collocation template means that the products of the specific tops, bottoms, shoes and bags are required to meet similar conditions before they constitute a qualified collocation under the template. Therefore, especially when seeking to produce matching under a limited product set (such as a certain brand, a certain store), the limited product candidates are not enough to meet the requirement that all parts of a specific template are similar, resulting in failure of matching output.
  • the template-based method can still produce limited combinations, and cannot cover the matching needs of all products on the platform.
  • a set of collocation usually consists of 3 to 5 products.
  • clothing, shoes, and bags usually cover more than 40 types, and there are as many as tens of millions of product candidates for each type. Therefore, the search space for a set of matching product candidates is huge, and the product permutations and combinations that can be used as combination candidates are inexhaustible.
  • the coverage of the method should reach more than 95%.
  • the method requires a higher pass rate while ensuring the coverage and richness of the output mix.
  • the calculation time required for a single product to return 10 sets and more collocations is less than 200ms.
  • FIG. 1 shows a schematic flowchart of a method for processing clothing matching information provided by an embodiment of the present application.
  • the execution subject of the solution provided in this embodiment may be a data object processing device, which may be a hardware with embedded program integrated on the client or server, or an application software installed on the client or server , It may also be tool software embedded in the client or server operating system, which is not limited in the embodiment of the present application.
  • the client can be any terminal device including a mobile phone, a tablet computer, a PDA (Personal Digital Assistant, personal digital assistant), a POS (Point of Sales, sales terminal), a car computer, etc.
  • the server can be a server, a cloud, etc.; this embodiment does not specifically limit this.
  • the method includes:
  • the above-mentioned first data object and second data object are specific clothing products, such as dresses, coats, shoes, bags, and so on.
  • the first data object and the second data object may be a product number or a product name for uniquely identifying a product.
  • the above steps can be triggered to be executed after the user performs any of the following actions:
  • the user sends a voice requesting to recommend an example of clothing matching for product A;
  • the user adds product A to the shopping cart
  • the user purchased commodity A; etc.
  • the object of the aforementioned user behavior—commodity A, is the first data object in this embodiment.
  • the server may execute the above steps after receiving the matching request sent by the client for the first data object.
  • the type to which the first data object belongs is the type of the commodity, such as shoes, clothing, bags, accessories (such as necklaces, headwear, earrings, rings, etc.), and so on. Because of the different wearing parts, clothing is divided into: tops, bottoms, conjoined and so on. Siamese category can be divided into: dress category, jumpsuit category and so on. Similarly, accessories can be divided into necklaces, headwear, earrings, rings, etc. because of the different wearing parts.
  • the first data object is clothing information
  • the type of clothing information is dress. Accordingly, at least one type of data object set as follows can be obtained:
  • Footwear data object set is a Footwear data object set, accessory data object set, upload data object set, bag data object set, download data object set, etc.
  • the technical solution provided in this embodiment may further include the following steps:
  • the sorting rule may specifically be: inverted by popularity points + product on-shelf time.
  • the number of selected data objects can be manually set, for example, ranking in the top 100, 150, 200 or more, which is not specifically limited in this embodiment.
  • the type of the data object (such as shoes, clothing, etc.) and the sorting parameters (such as popularity score, shelf time, etc.) can be recorded in the attributes of the data object. That is, taking the data object set of the first type that is different from the type of the first data object as an example, the process of obtaining the data object set of the first type is described, which specifically includes:
  • a plurality of data objects in the first order are selected to form the first type of data object set.
  • the attributes of the data object may include: type, style, color information, texture information, sales record, additional purchase rate, click rate, shelf time, suitable season, suitable occasion, suitable style, etc.
  • Popularity points can be calculated from sales records, additional purchase rates and click-through rates.
  • the second data object can be directly extracted from the data object set, and combined with the first data object to obtain multiple data object combinations.
  • the first data object is a dress
  • a shoe data object set is obtained in step 101.
  • the shoe data object set contains 200 data objects.
  • the 200 data objects contained in the data object set can be combined with the dress. Get 200 data object combinations.
  • the second data object can be extracted from part or all of the data object sets, and combined with the first data object to obtain multiple data object combinations.
  • the first data object is a dress
  • step 101 obtains a data object set of shoes, a bag data object set, and a jacket data object set.
  • one data object a as the second data object can be extracted from the shoe data object set, and one data object b as the second data object can be extracted from the bag data object set, and then the data object a and data object b Combine with the first data object to form a data object combination; alternatively, you can extract a data object c as the second data object from the shoe data object set, and extract a data object d as the second data object from the bag data object set , Extract a data object e as the second data object from the jacket data object set, and then combine the data object c, data object d, and data object e with the first data object into a data object combination; and so on.
  • the matching rule may include one or more rules, which is not specifically limited in this embodiment.
  • matching rules may include: rules for styles, rules for colors, rules for bright spots, and so on.
  • the above collocation rules can be obtained based on expert knowledge.
  • the style-specific rules are used to filter out unmatched data object combinations such as composition structure and seasonal temperature, such as the data object combination of "T-shirt” + “down pants”.
  • the color rules and the bright spot rules can be used to filter out data object combinations that do not meet the aesthetic requirements; the aesthetic requirements can be expert or expert's default dressing rules, such as no more than 3 main colors and no more than 1 bright spot. and many more.
  • the method provided in this embodiment further includes:
  • the purpose of adding the above steps is to further optimize the click-through rate of the match.
  • the popularity scores of the commodities contained in each clothing matching instance can be sales volume, click-through rate, additional purchase rate, etc., which can be adjusted according to different business goals
  • the score of each clothing matching instance can be calculated.
  • the average value of the popularity scores of other data objects other than the first data object in the included products is used as the final calculated score.
  • the technical solution provided in this embodiment extracts a second data object to be combined with the first data object from a set of at least one type of data object that is different from the type of the first data object to generate multiple data object combinations, and then use Matching rules, rather than strict matching templates, select clothing matching examples from multiple data object combinations that can be referenced by users. It is universally applicable and produces more rich combinations under the premise of ensuring matching quality. ; It can not only meet the actual matching needs of users, but also help to improve the commodity conversion rate of the e-commerce platform; in addition, through actual results, the technical solutions provided by the embodiments of this application can produce multiple sets of products for 99% of the target commodity pool Collocation suggestion, and the satisfaction rate of manual evaluation reaches 80%.
  • step 102 "extract a second data object to be combined with the first data object from the set of at least one type of data object, To generate multiple data object combinations" the following steps can be used to achieve:
  • a data object is selected from a part or all of the data object set of the at least one type of data object as the second data object and the first data object is combined into Data object combination; until the number of different data object combinations generated meets the preset number requirements.
  • At least one combination rule may be obtained based on the type of the first data object.
  • the obtained at least one combination rule may include: combination rules with shoes and bags 1. combination with jackets, shoes and bags Rule 2, Combination rule 3 with pants, shoes and bag combination, Combination rule 4 with coat, pants, shoes and bag combination, etc.
  • the symbol in Figure 3 Characterization is optional.
  • the obtained at least one combination rule may include: combination rule 1'with pants, shoes, and bags, and combination with skirts, shoes, and bags.
  • the combination rules corresponding to each type can be set in advance. In this way, when a user makes a purchase, view, or application matching request for a certain type of data object, he can directly retrieve the combination rule corresponding to the type of the data object.
  • step 1022 will be described by taking the at least one combination rule including: a first combination rule and a second combination rule as an example.
  • a first combination rule and a second combination rule are only two types of data object sets or more than two types of data object sets require two or more combination rules. That is, when at least two types of data object sets are acquired in step 101, the foregoing step 1022 may specifically be:
  • one data object is selected as the second data object and all the data object sets in the data object set of at least two types.
  • the first data object is combined into a data object combination; until the number of different data object combinations generated meets the preset number requirement.
  • this step 1022' can be implemented using the following steps:
  • N3 1 for data object set 3
  • N4 1 for data object set 4
  • N1 1 for data object set 1.
  • the preset number may be 100, 200, ... or more, which is not specifically limited in this embodiment.
  • the data object set specified by combination rule 1 includes: data object set 3 and data object set 4; the data object set specified by combination rule 2 includes: data object set 1, data object set 3, and data object set 4; combination The data object set specified by rule 3 includes: data object set 2, data object set 3, and data object set 4; the data object set specified by combination rule 4 includes: data object set 1, data object set 2, data object set 3, and data Object set 4.
  • the data object set specified by the combination rule can be one or more; among the multiple combination rules used at the same time, the data object set specified by each combination rule is different, but there may be part of the same data Object set.
  • the foregoing only shows the generation process of a combination instance. In fact, in what order is the data object extracted from each data object set to be combined with the first data object, this embodiment does not specifically limit it, as long as The data object combination that meets the quantity requirement is finally obtained, and these data object combinations are all different.
  • step 103 of the method provided in this embodiment "from the multiple data object combinations, select a data object combination that meets the matching rules as an example of clothing matching for the user's reference.”
  • the attributes of the data object include at least one of the following attribute items: type, style, color information, texture information, sales records, additional purchase rate, click rate, and shelf time.
  • type, style, etc. of the attributes of the data object can be obtained from the information filled in by the merchant; the color information and texture information can be identified by the corresponding algorithm, and the sales record, additional purchase rate, click-through rate and shelf time can be obtained. Obtained from the server.
  • This embodiment does not specifically limit the method for obtaining the attribute of the data object, and the corresponding technology in the prior art can be used to obtain it.
  • the aforementioned filtering rules may include, but are not limited to: rules for styles, rules for colors, rules for bright spots, and so on.
  • step 1032 may include: using style-specific rules to determine whether the attribute combination of the data object contained in the first data object combination among the multiple data object combinations meets all State the rules for styles. Further, the step of "using the style-specific rule to determine whether the attribute combination of the data object contained in the first data object combination among the plurality of data object combinations meets the style-specific rule" may specifically be:
  • step 1033 "deleting the data object combination whose attribute combination of the data object contained in the multiple data object combination meets the filtering rule" includes:
  • the first data object combination is deleted.
  • the above step 1032 may include: using the color-specific rule to determine whether the attribute combination of the data object contained in the first data object combination among the multiple data object combinations meets all State the rules for styles. Further, the step of "using the color-specific rule to determine whether the attribute combination of the data object contained in the first data object combination of the plurality of data object combinations meets the style-specific rule" may specifically include:
  • step 1033 "deleting the data object combination whose attribute combination of the data object contained in the multiple data object combination meets the filtering rule" includes:
  • the first data object combination is deleted.
  • the above step 1032 may include: using the rule for a bright spot, determining whether the attribute combination of the data object contained in the first data object combination among the plurality of data object combinations meets the target Bright spot rules. Further, the step of "using the rule for bright spots to determine whether the attribute combination of the data objects contained in the first data object combination among the plurality of data object combinations meets the rule for bright spots" may specifically include:
  • step 1033 "deleting the data object combination whose attribute combination of the data object contained in the multiple data object combination meets the filtering rule" includes:
  • the first data object combination is deleted.
  • the technical solution provided by this embodiment can be simply summarized as: a process of data object combination construction-filtering-sorting.
  • the clothing knowledge of clothing experts or experts can be modeled into the execution logic of these three links, which realizes universal automatic calculation and output of matching.
  • the combination rule is introduced in the data object combination structure part, and the potential clothing matching example candidates are quickly constructed in the huge product arrangement and combination space to solve the computational complexity problem.
  • the filtering rules are used to accurately filter out the clothing matching examples that meet the decent and aesthetic requirements in daily wear from the combination of multiple data objects, so as to solve the problem of matching quality.
  • the data object combination structure part it is based on the combination rules deposited by clothing experts or masters to guide the user to request the product and which types of products are combined to form a data object combination.
  • the user requested a dress.
  • the dress can be empty or jacket for the top part, and empty or trousers for the bottom part. Shoes and bags are necessary.
  • the 4 parts of the commodity (coat, pants, shoes and bag) in the candidate commodity pool can be called out in the storage module, such as 200 pieces of each kind of commodity, as the data object set of each kind of commodity. Generate collocation candidates.
  • the filtering part can use filtering rules to filter out inappropriate data object combinations that do not conform to aesthetic principles in color and texture.
  • the filtering rules may include, but are not limited to, at least one of the following: filtering rules for styles, filtering rules for colors, filtering rules for bright spots, and so on.
  • the filtering rules for styles can be simply understood as the style matching relationship represented in the table shown in FIG. 4.
  • FIG. 4 Refer to the table format shown in Figure 4, which is composed of combinations of styles precipitated by experts or experts that cannot be worn at the same time, which can generate 2000 or more grid points; Figure 4 only shows the matching relationship of some styles.
  • the role of the filtering rules for styles is to filter out data object combinations that do not meet the appropriate requirements in terms of composition structure and seasonal temperature.
  • the data object combination is usually a set of 3 to 5 pieces.
  • the data object combination is checked in pairs for all the products contained in the data object combination. If a combination that does not meet the filtering rules for styles is encountered, the data object combination needs to be deleted.
  • a certain data object combination includes: coat, top and trousers; correspondingly, query whether the coat and top meet the filtering rules for styles, whether the top and trousers meet the filtering rules for styles, and whether the jackets and trousers meet Filtering rules for styles. If a combination of jacket and top, top and trousers, and jacket and trousers is a combination that is not suitable for wearing at the same time through the query, the data object combination needs to be filtered out, that is, deleted from multiple data object combinations .
  • the color information of each product will contain 1 to 3 colors
  • the texture information will contain 1 to 3 texture tags, as follows:
  • Color information (Color1,AreaC1), (Color2,AreaC2)...
  • Texture information (Texture1,AreaT1), (Texture2,AreaT2)...
  • Color is a color label, such as carmine
  • the corresponding Area is the proportion of the area of the color label in the product, such as 40%
  • Texture is a texture label, such as polka dots
  • the corresponding Area is the texture label in The area percentage of the product, such as 70%.
  • the color filtering rule can be specifically as follows: more than 3 main colors in the data object combination need to be filtered out.
  • the implementation of this rule is to traverse each product in a data object combination in turn, and count the number of dominant colors. When the number of dominant colors of different types is greater than 3, the data object combination needs to be filtered out, that is, from multiple Deleted from the combination of data objects.
  • the filtering rule for bright spots may be specifically as follows: more than one bright spot in the data object combination needs to be filtered out.
  • the implementation of this rule is to traverse each product in a data object combination in turn, and count the number of bright spots. When the total number of bright spots of different types is greater than 1, the data object combination needs to be filtered out, that is, from multiple data objects Delete from the combination.
  • the remaining data object combinations that are not filtered out are examples of clothing matching that can be referenced by the user.
  • the remaining half can be provided to users as examples of clothing matching.
  • these clothing matching examples can be sorted to determine the final display order for users.
  • this step is to further optimize the click-through rate of the match.
  • the matching score can be calculated according to the popularity score of each product in each clothing matching instance (which can be sales, click-through rate, additional purchase rate, etc., which can be adjusted according to different business goals), and then sorted according to the matching score. Then display it to users in order from high to low.
  • FIG. 5 shows a schematic flowchart of a data object processing method provided by another embodiment of the present application. As shown in the figure, the data object processing method includes:
  • the attribute of the first data object and the attribute of the at least one second data object determine whether the at least one second data object is matched with the first data object.
  • each type of second data object in at least one type of second data object can be obtained in a corresponding type of data object set.
  • the first data object and the second data object may be clothing products, such as dresses, coats, shoes, bags, and so on.
  • the first data object and the second data object may be a product number, a product name, etc. used to uniquely identify a product.
  • the attribute may include at least one of the following: location, style, color information, texture information, sales record, additional purchase rate, click rate, and shelf time.
  • this step 202 may include at least one of the following implementation steps:
  • the style of the first data object and the style of any second data object in the at least one second data object are determined whether the first data object and the at least one second data object match;
  • the technical solution provided in this embodiment extracts a second data object to be combined with the first data object from a set of at least one type of data object that is different from the type of the first data object to generate multiple data object combinations, and then use Matching rules, rather than strict matching templates, select clothing matching examples from multiple data object combinations that can be referenced by users. It is universally applicable and produces more rich combinations under the premise of ensuring matching quality.
  • the technical solutions provided by the embodiments of this application can produce multiple sets of matching suggestions for 99% of the products in the target product pool, and the satisfaction rate of manual evaluation reaches 80%.
  • the technical solutions provided by the embodiments of the present application can be applied in a clothing matching recommendation scenario.
  • a user selects a dress through a client application (APP), and adds the selected dress to a shopping cart.
  • the client monitors the user's behavior, it recommends coats, shoes, bags, etc. that can be matched with the dress; and displays the page information corresponding to these coats, shoes, and bags that can be matched with the dress as recommended information on the user interface on.
  • the client sends a matching request to the server for the dress.
  • the server can use the methods provided in the foregoing embodiments to feed back to the client the coats, shoes, bags, etc. that can be recommended for the user. That is, the technical solution provided by this embodiment can also be implemented by the following hardware system architecture.
  • the clothing matching information processing system includes:
  • the client 301 is configured to send request information to the server in response to the operation performed by the user on the first data object; receive the clothing matching instance fed back by the server for the first data object; and provide the clothing matching instance To the user;
  • the server 302 is configured to obtain request information sent by the client for the first data object; extract from a set of at least one type of data object that is different from the type of the first data object to be used with the first data object.
  • the second data object of the object combination to generate a plurality of data object combinations; and the data object combination that meets the matching rules among the plurality of data object combinations is fed back to the client as a clothing matching instance.
  • the technical solution provided in this embodiment extracts a second data object to be combined with the first data object from a set of at least one type of data object that is different from the type of the first data object to generate multiple data object combinations, and then use Matching rules, rather than strict matching templates, select clothing matching examples from multiple data object combinations that can be referenced by users. It is universally applicable and produces more rich combinations under the premise of ensuring matching quality.
  • the technical solutions provided by the embodiments of this application can produce multiple sets of matching suggestions for 99% of the products in the target product pool, and the satisfaction rate of manual evaluation reaches 80%.
  • FIG. 7 shows a schematic flowchart of the method for processing the clothing matching information provided by an embodiment of the present application.
  • the execution subject of the method provided in this embodiment may be a client, which may be a mobile phone, a tablet computer, a PDA (Personal Digital Assistant), a POS (Point of Sales, sales terminal), a car computer, etc. Any terminal device.
  • the method includes:
  • the clothing matching instance is a combination of data objects that meet the matching rules selected from a combination of multiple data objects; the combination of multiple data objects is a combination of at least one type that is different from the type of the first data object. Is generated by combining the second data object extracted from the data object set with the first data object.
  • the designated operation may include: the user clicks the corresponding control key, the user makes a control voice for the first data object, or the user makes a designated action, which is not specifically limited in this embodiment.
  • the technical solution provided in this embodiment extracts a second data object to be combined with the first data object from a set of at least one type of data object that is different from the type of the first data object to generate multiple data object combinations, and then use Matching rules, rather than strict matching templates, select clothing matching examples from multiple data object combinations that can be referenced by users. It is universally applicable and produces more rich combinations under the premise of ensuring matching quality.
  • the technical solutions provided by the embodiments of this application can produce multiple sets of matching suggestions for 99% of the products in the target product pool, and the satisfaction rate of manual evaluation reaches 80%.
  • step 403 "provide the clothing matching instance to the user” may include at least one of the following:
  • FIG. 8 shows a schematic flowchart of a method for processing clothing matching information provided by an embodiment of the present application.
  • the execution subject of the method provided in this embodiment may be a server, and the server may be a common server, a virtual server, or the cloud, etc., which is not specifically limited in this embodiment.
  • the method includes:
  • the method provided in this embodiment can also implement other steps described in the above embodiments.
  • the method provided in this embodiment can also implement other steps described in the above embodiments.
  • the technical solution provided in this embodiment extracts a second data object to be combined with the first data object from a set of at least one type of data object that is different from the type of the first data object to generate multiple data object combinations, and then use Matching rules, rather than stringent matching templates, select clothing matching examples from multiple data object combinations that can be used for users to refer to.
  • the universality is strong, and the matching output is more rich under the premise of ensuring matching quality
  • the technical solutions provided by the embodiments of this application can produce multiple sets of matching suggestions for 99% of the products in the target product pool, and the satisfaction rate of manual evaluation reaches 80%.
  • FIG. 9 shows a schematic structural diagram of an apparatus for processing clothing matching information provided by an embodiment of the present application.
  • the data object processing device includes: an acquisition module 11, a combination module 12 and a selection module 13.
  • the acquisition module 11 is used to acquire at least one type of data object set different from the type of the first data object
  • the combination module 12 is used to extract from the at least one type of data object set to be used with the The second data object of the first data object combination is used to generate multiple data object combinations
  • the selection module 13 is used to select a data object combination that meets the matching rules from the multiple data object combinations as a reference for the user Examples of clothing matching.
  • the technical solution provided in this embodiment extracts a second data object to be combined with the first data object from a set of at least one type of data object that is different from the type of the first data object to generate multiple data object combinations, and then use Matching rules, rather than stringent matching templates, select clothing matching examples from multiple data object combinations that can be used for users to refer to.
  • the universality is strong, and the matching output is more rich under the premise of ensuring matching quality
  • the technical solutions provided by the embodiments of this application can produce multiple sets of matching suggestions for 99% of the products in the target product pool, and the satisfaction rate of manual evaluation reaches 80%.
  • combination module 12 may be specifically used for:
  • one data object is selected as a second data object from a part of the data object set or all data object sets in the data object set of the at least one type, and the first data object is combined into a data object Combination; until the number of different data object combinations generated meets the preset number requirements.
  • the at least one combination rule includes: a first combination rule and a second combination rule; and the combination module 12 is further configured to:
  • selection module 13 is also used for:
  • the data object combination whose attribute combination of the data object contained in the multiple data object combination meets the filtering rule is deleted.
  • the attributes of the data object include at least one of the following attribute items: type, style, color information, texture information, sales record, additional purchase rate, click rate, and shelf time.
  • the filtering rules include: style-specific rules; correspondingly, the selection module 13 is also used for:
  • the selection module 13 is further configured to: when the style combination of the first data object and the style combination of any second data object in the at least one second data object meet the style-specific rule, change The first data object combination is deleted.
  • filtering rules include: rules for colors; and the selection module 13 is also used for:
  • the selection module is further configured to delete the first data object combination when the number of dominant colors is greater than a first threshold.
  • filtering rules include: rules for bright spots; and the selection module 13 is also used for:
  • selection module 13 is also used for:
  • the first data object combination is deleted.
  • the acquisition module is also used for:
  • a plurality of data objects in the first order are selected to form the first type of data object set.
  • the method provided in this embodiment further includes: a sorting module.
  • the sorting module is used for sorting a plurality of clothing matching instances; according to the sorting results, providing a plurality of clothing matching instances to the user.
  • the apparatus for processing clothing matching information provided in the foregoing embodiment can implement the technical solutions described in the foregoing method embodiments.
  • the foregoing modules or units please refer to the corresponding content in the foregoing method embodiments. , I won’t repeat it here.
  • FIG. 10 shows a schematic structural diagram of a data object processing apparatus provided by an embodiment of the present application.
  • the data object processing device includes: an acquisition module 21, a determination module 22, and a provision module 23.
  • the obtaining module 21 is used to obtain at least one type of second data object that is different from the type of the first data object;
  • the determining module 22 is used to obtain according to the attributes of the first data object and the at least one first data object. 2.
  • the attribute of the data object determining whether the at least one second data object is matched with the first data object; the providing module 23 is used when the at least one second data object is matched with the first data object, The at least one second data object is provided to the user.
  • the technical solution provided in this embodiment extracts a second data object to be combined with the first data object from a set of at least one type of data object that is different from the type of the first data object to generate multiple data object combinations, and then use Matching rules, rather than strict matching templates, select clothing matching examples from multiple data object combinations that can be referenced by users. It is universally applicable and produces more rich combinations under the premise of ensuring matching quality.
  • the technical solutions provided by the embodiments of this application can produce multiple sets of matching suggestions for 99% of the products in the target product pool, and the satisfaction rate of manual evaluation reaches 80%.
  • the first data object and the second data object are specific clothing products, and correspondingly, the attributes of the first data object and the second data object may specifically include at least one of the following: , Style, color information, texture information, sales records, additional purchase rate, click rate, and shelf time; correspondingly, the determination module 22 is also used for:
  • the number of the main colors determine whether the first data object and the at least one second data object match; and/or
  • the color information of the first data object and the color information of the at least one second data object determine whether there is a color bright spot as the first determination result; according to the texture information of the first data object and the at least one second data object The texture information of the data object determines whether there is a texture bright spot as the second determination result, and according to the first determination result and the second determination result, it is determined whether the first data object and the at least one second data object match.
  • FIG. 11 shows a schematic structural diagram of an apparatus for processing clothing matching information provided by an embodiment of the present application.
  • the data object processing device includes: a sending module 31, a receiving module 32, and an output module 33.
  • the sending module 31 is configured to send request information to the server in response to the operation performed by the user on the first data object;
  • the receiving module 32 is configured to receive the clothing matching instance that the server feedbacks on the first data object;
  • the output module 33 is used to provide the clothing matching examples to the user.
  • the clothing matching instance is a combination of data objects that meet the matching rules selected from a combination of multiple data objects;
  • the combination of multiple data objects is a combination of at least one type that is different from the type of the first data object. Is generated by combining the second data object extracted from the data object set with the first data object.
  • the output module is also used for:
  • the technical solution provided in this embodiment extracts a second data object to be combined with the first data object from a set of at least one type of data object that is different from the type of the first data object to generate multiple data object combinations, and then use Matching rules, rather than strict matching templates, select clothing matching examples from multiple data object combinations that can be referenced by users. It is universally applicable and produces more rich combinations under the premise of ensuring matching quality.
  • the technical solutions provided by the embodiments of this application can produce multiple sets of matching suggestions for 99% of the products in the target product pool, and the satisfaction rate of manual evaluation reaches 80%.
  • the apparatus for processing clothing matching information provided in the foregoing embodiment can implement the technical solutions described in the foregoing method embodiments.
  • the foregoing modules or units please refer to the corresponding content in the foregoing method embodiments. , I won’t repeat it here.
  • FIG. 12 shows a schematic structural diagram of an apparatus for processing clothing matching information provided by an embodiment of the present application.
  • the data object processing device includes: an acquisition module 41, an extraction module 42, and a feedback module 43.
  • the acquiring module 41 is used to acquire the request information sent by the client for the first data object;
  • the extracting module 42 is used to collect data objects of at least one type different from the type of the first data object. , Extracting a second data object to be combined with the first data object to generate a plurality of data object combinations;
  • the feedback module 43 is configured to use the data object combination that meets the collocation rule among the plurality of data object combinations as The clothing matching examples are fed back to the client.
  • the technical solution provided in this embodiment extracts a second data object to be combined with the first data object from a set of at least one type of data object that is different from the type of the first data object to generate multiple data object combinations, and then use Matching rules, rather than strict matching templates, select clothing matching examples from multiple data object combinations that can be referenced by users. It is universally applicable and produces more rich combinations under the premise of ensuring matching quality.
  • the technical solutions provided by the embodiments of this application can produce multiple sets of matching suggestions for 99% of the products in the target product pool, and the satisfaction rate of manual evaluation reaches 80%.
  • the apparatus for processing clothing matching information provided in the foregoing embodiment can implement the technical solutions described in the foregoing method embodiments.
  • the foregoing modules or units please refer to the corresponding content in the foregoing method embodiments. , I won’t repeat it here.
  • FIG. 13 shows a schematic structural diagram of an electronic device provided by an embodiment of the present application.
  • the electronic device includes a memory 51 and a processor 52.
  • the memory 51 may be configured to store other various data objects to support operations on the electronic device. Examples of these data objects include instructions for any application or method operating on an electronic device.
  • the memory 51 can be implemented by any type of volatile or non-volatile storage device or their combination, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable and Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Magnetic Disk or Optical Disk.
  • SRAM static random access memory
  • EEPROM electrically erasable programmable read-only memory
  • EPROM erasable and Programmable Read Only Memory
  • PROM Programmable Read Only Memory
  • ROM Read Only Memory
  • Magnetic Memory Flash Memory
  • Magnetic Disk Magnetic Disk or Optical Disk.
  • the processor 52 is coupled with the memory 51, and is configured to execute the program stored in the memory 51 for:
  • a data object combination that meets the matching rules is selected as a clothing matching example that can be referenced by the user.
  • processor 52 executes the program in the memory 51, in addition to the above functions, it may also implement other functions. For details, please refer to the descriptions of the previous embodiments.
  • the electronic device further includes: a display 54, a communication component 53, a power supply component 55, an audio component 56 and other components. Only part of the components are schematically shown in FIG. 13, which does not mean that the electronic device only includes the components shown in FIG.
  • An embodiment of the present application also provides an electronic device.
  • the structure of the electronic device provided in this embodiment is similar to the structure of the foregoing electronic device embodiment, as shown in FIG. 13.
  • the electronic device includes a memory and a processor.
  • the processor is coupled with the memory, and is configured to execute the program stored in the memory for:
  • the at least one second data object is matched with the first data object, the at least one second data object is provided to the user.
  • An embodiment of the present application also provides a client device.
  • the structure of the client device provided in this embodiment is similar to the structure of the foregoing electronic device embodiment, as shown in FIG. 13.
  • the client device includes a memory and a processor.
  • the processor is coupled with the memory, and is configured to execute the program stored in the memory for:
  • the clothing matching instance is a combination of data objects that meet the matching rules selected from a combination of multiple data objects; the combination of multiple data objects is a combination of at least one type that is different from the type of the first data object. Is generated by combining the second data object extracted from the data object set with the first data object.
  • An embodiment of the present application also provides a server device.
  • the structure of the server device provided in this embodiment is similar to the structure of the foregoing electronic device embodiment, as shown in FIG. 13.
  • the server device includes a memory and a processor.
  • the processor is coupled with the memory, and is configured to execute the program stored in the memory for:
  • the data object combination that meets the matching rule among the multiple data object combinations is fed back to the client as a clothing matching instance.
  • an embodiment of the present application also provides a computer-readable storage medium storing a computer program, which can realize the steps or functions of the clothing matching information processing method provided by the foregoing embodiments when the computer program is executed by a computer.
  • the embodiments of the present application also provide a computer-readable storage medium storing a computer program, which can realize the steps or functions of the data object processing method provided by the foregoing embodiments when the computer program is executed by a computer.
  • the device embodiments described above are merely illustrative.
  • the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, they may be located in One place, or it can be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the objectives of the solutions of the embodiments. Those of ordinary skill in the art can understand and implement without creative work.
  • each implementation manner can be implemented by software plus a necessary general hardware platform, and of course, it can also be implemented by hardware.
  • the computer software product can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic A disc, an optical disc, etc., include a number of instructions to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiment.

Abstract

L'invention concerne un procédé, un système et un dispositif de traitement d'informations de collocation de vêtements, et un procédé, un système et un dispositif de traitement d'objets de données. Le procédé de traitement d'informations de collocation de vêtements comprend les étapes suivantes consistant : à acquérir un ensemble d'objets de données d'au moins un type qui diffère du type d'un premier objet de données (101) ; à extraire, de l'ensemble d'objets de données desdits types, un second objet de données à combiner avec le premier objet de données, de façon à générer une pluralité de combinaisons d'objets de données (102) ; et à sélectionner, parmi la pluralité de combinaisons d'objets de données, une combinaison d'objets de données qui se conforme à une règle de collocation pour servir d'instance de collocation de vêtements qui peut être considérée comme référence par un utilisateur (103). Le procédé présente une forte universalité, a des collocations plus abondantes produites sur la base de la garantie de la qualité de collocation, et a un degré de satisfaction de collocation relativement élevé.
PCT/CN2020/073140 2019-01-31 2020-01-20 Procédé, système et dispositif de traitement d'informations de collocation de vêtements, et procédé, système et dispositif de traitement d'objets de données WO2020156306A1 (fr)

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