WO2020156306A1 - Clothing collocation information processing method, system and device, and data object processing method, system and device - Google Patents

Abstract

A clothing collocation information processing method, system and device, and a data object processing method, system and device. The clothing collocation information processing method comprises the following steps: acquiring a data object set of at least one type that differs from the type of a first data object (101); extracting, from the data object set of the at least one type, a second data object for combining with the first data object, so as to generate a plurality of data object combinations (102); and selecting, from the plurality of data object combinations, a data object combination that conforms to a collocation rule to serve as a clothing collocation instance that can be taken as a reference by a user (103). The method has strong universality, has more abundant collocations produced on the premise of ensuring the collocation quality, and has a relatively high collocation satisfaction degree.

Description

Clothing matching information processing method, data object processing method, system and equipment

This application claims the priority of a Chinese patent application filed on January 31, 2019 with the application number 201910101037.6 and the title of the invention "processing method of clothing matching information, data object processing method, system and equipment", the entire content of which is incorporated by reference In this application.

Technical field

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.

Background technique

For a long time, people have been accustomed to choosing products that suit them through repeated selections from a wide range of satisfying, styles and colors. Most ordinary users don't know how to match these products, such as what kind of shoes to match with dresses, and what style of bags to match with.

At present, 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.

Summary of the invention

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.

In an embodiment of the present application, a method for processing clothing matching information is provided. The method includes:

Acquiring a data object set of at least one type different from the type to which the first data object belongs;

Extracting a second data object to be combined with the first data object from the at least one type of data object set to generate a plurality of data object combinations;

From the multiple data object combinations, a data object combination that meets the matching rules is selected as a clothing matching example that can be referenced by the user.

In another embodiment of the present application, a data object processing method is provided. The method includes:

When a specified event related to the first data object occurs, acquiring at least one type of second data object that is different from the type to which the first data object belongs;

Determine whether the at least one second data object is matched with the first data object according to the attributes of the first data object and the attributes of the at least one second data object;

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.

In another embodiment of the present application, a method for processing clothing matching information is provided. The method includes:

In response to the user's operation on the first data object, sending request information to the server;

Receiving a clothing matching instance fed back by the server for the first data object;

Providing the clothing matching example to the user;

Wherein, 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.

In another embodiment of the present application, a method for processing clothing matching information is provided. The method includes:

Acquiring request information sent by the client for the first data object;

Extracting 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 to which the first data object belongs to generate multiple data object combinations;

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.

In an embodiment of the present application, a system for processing clothing matching information is provided. The system 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.

In an embodiment of the present application, an electronic device is provided. The 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:

Acquiring a data object set of at least one type different from the type to which the first data object belongs;

Extracting a second data object to be combined with the first data object from the at least one type of data object set to generate a plurality of data object combinations;

From the multiple data object combinations, a data object combination that meets the matching rules is selected as a clothing matching example that can be referenced by the user.

In another embodiment of the present application, an electronic device is provided. The 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:

Acquiring a second data object of at least one type different from the type to which the first data object belongs;

Determine whether the at least one second data object is matched with the first data object according to the attributes of the first data object and the attributes of the at least one second data object;

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.

In an embodiment of the present application, a client device is provided. The client 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:

In response to the user's operation on the first data object, sending request information to the server;

Receiving a clothing matching instance fed back by the server for the first data object;

Providing the clothing matching example to the user;

Wherein, 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.

In an embodiment of the present application, a server device is provided. The 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:

Acquiring request information sent by the client for the first data object;

Extracting 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 to which the first data object belongs to generate a plurality of data object combinations;

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 In addition, through actual results, 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%.

Description of the drawings

In order to more clearly describe the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description These are some embodiments of the application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work.

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;

5 is a schematic flowchart of a data object processing method provided by another embodiment of the application;

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;

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;

10 is a schematic structural diagram of a data object processing device provided by another embodiment of the application;

11 is a schematic structural diagram of a clothing matching information processing device provided by another embodiment of the application;

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.

detailed description

In order to enable those skilled in the art to better understand the solutions of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the drawings in the embodiments of the present application.

Some processes described in the specification, claims, and the above-mentioned drawings of this application include multiple operations appearing in a specific order, and these operations may be performed out of the order in which they appear in this document or in parallel. The sequence numbers of operations, such as 101, 102, etc., are only used to distinguish different operations, and the sequence numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions of "first" and "second" in this article are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit the "first" and "second" Are different types. In addition, the following embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative work are within the protection scope of this application.

The prior art usually adopts the following three schemes to implement clothing matching recommendation. The following will explain separately:

The first one is based on manual editing

Based on the manual editing scheme, the quality is high, but the number of matching is very small. By mobilizing experts and talents and other groups to make hand-made collocations, 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.

The second, based on related purchases

Plans based on related purchases are of average quality and large in quantity. By mining the products that users have jointly purchased in a period of time, 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. In view of the very few problems with the products of the first scheme, 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. However, 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. For example, 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. In summary, the template-based method can still produce limited combinations, and cannot cover the matching needs of all products on the platform.

In order to provide users with richer and more satisfying matching results, the technical challenges to be overcome are:

A. Computational complexity

A set of collocation usually consists of 3 to 5 products. For e-commerce platforms, 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.

B. Product coverage

On the premise that there is a sufficient set of candidate products, if the method fails to produce a matching product for a certain product, an opportunity for users to display matching and product conversion will be missed. Therefore, for the massive clothing products on the platform, the coverage of the method should reach more than 95%.

C. Matching quality

If the matching suggestions surrounding the display of the products requested by the user do not meet the basic aesthetic requirements of clothing, the user will not continue to browse and click. Therefore, the method requires a higher pass rate while ensuring the coverage and richness of the output mix.

D. Achieve efficiency

For each of the massive clothing products on the e-commerce platform, matching services are provided, and real-time computing capabilities must be provided, instead of caching the calculation results in advance (the enumeration is too large and the cost is too high). Therefore, 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. Among them, 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. As shown in Figure 1, the method includes:

101. Acquire a data object set of at least one type different from the type to which the first data object belongs.

102. Extract a second data object to be combined with the first data object from the set of data objects of the at least one type to generate multiple data object combinations.

103. From the multiple data object combinations, select a data object combination that meets the matching rules as an example of clothing matching that can be referenced by the user.

In the clothing e-commerce scenario, 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. For the client, the above steps can be triggered to be executed after the user performs any of the following actions:

The user clicks on the control requesting to view the clothing matching example of product A;

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 pays attention to product A;

The user purchased commodity A; etc.

The object of the aforementioned user behavior—commodity A, is the first data object in this embodiment.

For the server, the server may execute the above steps after receiving the matching request sent by the client for the first data object.

This embodiment can be applied to many application scenarios, such as apparel-type e-commerce scenarios. 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.

Assuming that in a clothing e-commerce scene, the first data object is clothing information, and 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, accessory data object set, upload data object set, bag data object set, download data object set, etc.

For example, acquiring the data object set of shoes and the data object set of bags. In specific implementation, the data objects (ie, commodities) contained in various data object sets may be popular or newly launched commodities. In an achievable technical solution, the technical solution provided in this embodiment may further include the following steps:

Collect data objects of the same type;

Sort the collected data objects according to the sorting rules;

Select the top multiple data objects to obtain the data object set of this type.

For example, 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:

Acquiring the attributes of each data object in the first type data object pool;

Sort the data objects in the first type data object pool according to the attributes of each data object in the first type data object pool;

A plurality of data objects in the first order are selected to form the first type of data object set.

Wherein, 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.

In 102 above, when there is one type of data object set, 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. For example, if 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.

When there are two or more data object sets, 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. For example, the first data object is a dress, and step 101 obtains a data object set of shoes, a bag data object set, and a jacket data object set. In specific implementation, 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.

What needs to be explained here is: How to extract data objects from at least one type of data object set to generate a data object combination is not specifically limited in this embodiment, as long as it is ensured that all data objects included in the data object combination are of different types, And multiple data object combinations are different.

In the above 103, the matching rule may include one or more rules, which is not specifically limited in this embodiment. In different application scenarios, the setting of matching rules will be different. For example, in the e-commerce scenario of shoes, clothes and bags, 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. Specifically, 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.

In addition, assuming that there are multiple instances of clothing matching that can be referenced by the user, the method provided in this embodiment further includes:

Sort multiple instances of clothing collocation;

According to the sorting result, multiple instances of clothing matching are provided to the user.

The purpose of adding the above steps is to further optimize the click-through rate of the match. During specific implementation, 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) can be obtained, and the score of each clothing matching instance can be calculated. For example, 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. Then, according to the size of the score, sort the instances with the higher score first and the lower score second to sort the multiple clothing collocation examples. Finally, it can be displayed to users in descending order of 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%.

In an achievable technical solution, in the method provided in this embodiment, 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:

1021. Acquire at least one combination rule;

1022. Using the at least one combination rule, 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.

In the above 1021, at least one combination rule may be obtained based on the type of the first data object. As shown in Figures 2 and 3, assuming that the type of the first data object belongs to the dress category, 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. Among them, the symbol in Figure 3

Characterization is optional. As shown in Figure 3, assuming that the type of the first data object is an upper garment category, the obtained at least one combination rule may include: combination rule 1'with pants, shoes, and bags, and combination with skirts, shoes, and bags. Combination rule 2', combination rule 3'with jackets, skirts, shoes and bags, etc.

In practical applications, 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.

Hereinafter, the above 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. Generally, 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:

1022'. Using the first combination rule and the second combination rule, 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.

Specifically, this step 1022' can be implemented using the following steps:

S1. Obtain the current sorting sequence number Ni corresponding to each type of data object set in the at least two types of data object sets, where the i values of different types of data object sets are different;

S2. From the set of all data objects specified by the first combination rule, obtain the data objects ranked as Ni as the second data object and combine the first data object into a data object combination, and specify the first combination rule The corresponding Ni of all data object sets of is updated to Ni+1;

S3. From the set of all data objects specified by the second combination rule, obtain the data objects ranked as Ni as the second data object and combine the first data object into a data object combination, and combine the second combination rule The Ni corresponding to all the specified data object sets are updated to Ni+1;

S4. Repeat the above steps until the number of generated different combination instances meets the preset number requirement.

The above steps S1 to S4 can be simply understood as the following process:

Referring to the example shown in Fig. 2, N3=1 corresponding to data object set 3 of shoes, N4=1 corresponding to data object set 4 of bags, and N1=1 corresponding to data object set 1 of outerwear;

First, obtain the Ni of each data object set; N3=1 for data object set 3, N4=1 for data object set 4, and N1=1 for data object set 1.

Then, from the data object set 3 specified by the first rule, obtain the products with a ranking of N3=1; from the data object set 4 specified by the first rule, obtain the commodities with a ranking of N4=1 to form a data object combination (31 ,41); and update N3 to 1+1=2; update N4 to 1+1=2;

Subsequently, from the data object set 3 specified by the second rule, obtain the products with a ranking of N3=2; from the data object set 4 specified by the second rule, obtain the commodities with the ranking of N4=2; In the data object set 1, obtain the products with the sort N1=1 to form a data object combination (11, 32, 42); and add N3, N4, and N1 to the existing basis and then +1, that is, update N3 to 3. N4 is updated to 3, and N1 is updated to 2.

Here only describes the process of generating different combination examples when there are two combination rules. When there are third combination rules, fourth combination rules, ... and more combination rules, the principle type. The following is an example of 4 combination rules shown in Figure 2. The process of generating multiple combined instances can be specifically:

Using the combination rule 1, extract the products 31 with the sort sequence number N=1 from the data object set 3, and extract the products 41 with the sort sequence number N=1 from the data object set 4. The dress 00 and product 31 that the user purchases, browses or requests are matched Compose a 3-piece data object combination with commodity 41 [00, 31, 41];

Using the combination rule 2, extract the products 32 with the sort sequence number N+1=2 from the data object set 3, extract the products 42 with the sort sequence number N+1=2 from the data object set 4, and extract the sort sequence number N from the data object set 1. Commodity 11 = 1, combine dress 00, commodity 32, commodity 42, and commodity 11 into a 4-piece data object combination [00, 32, 42, 11];

Using the combination rule 3, extract the products 33 with the sort sequence number N+2=3 from the data object set 3, extract the products 43 with the sort sequence number N+2=3 from the data object set 4, and extract the sort sequence number N from the data object set 2. =1 product 21, the dress 00, product 33, product 43, and product 21 form a 4-piece data object combination [00, 33, 43, 21];

Using the combination rule 4, extract the products 34 with the sort sequence number N+3=4 from the data object set 3, extract the products 44 with the sort sequence number N+3=4 from the data object set 4, and extract the sort sequence number N from the data object set 1. +1=2 product 12, extract the product 22 with the sequence number N+1=2 from the data object set 2, and combine dress 00, product 34, product 44, product 12, and product 22 into a 4-piece data object combination [ 00, 34, 44, 12, 22].

Update the sequence number N to N+1, and repeat the above steps until the number of different combination instances generated meets the preset number requirement.

During specific implementation, the preset number may be 100, 200, ... or more, which is not specifically limited in this embodiment.

In the above example, 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. What needs to be explained here is: in the specific implementation, 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. In addition, 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.

Further, in 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." Method realization:

1031. Acquire attributes of data objects contained in each data object combination in the multiple data object combinations.

1032. Use a filter rule for at least one attribute item in the attributes to determine whether the attribute combination of the data object contained in each data object combination in the multiple data object combinations meets the filter rule.

1033. Delete the data object combination whose attribute combination of the data objects included in the multiple data object combinations meets the filtering rule.

In a specific application scenario, 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. What needs to be explained here is: the 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.

When the filtering rules include style-specific rules, 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:

Acquiring the style of the first data object included in the first data object combination and the style of at least one second data object combined with the first data object;

It is determined whether the combination of the style of the first data object and the style of each second data object in the at least one second data object complies with the style-specific rule.

Correspondingly, the above 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:

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 meets the style-specific rule, the first data object combination is deleted.

When the filtering rule includes a color-specific rule, 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:

Acquiring the color information of the first data object included in the first data object combination and the color information of at least one second data object combined with the first data object;

Based on the color information of the first data object and the color information of the at least one second data object, determining the number of main colors that meet the main color condition;

It is determined whether the number of main colors meets the color-specific rule.

Correspondingly, the above 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:

When the number of dominant colors is greater than a first threshold, the first data object combination is deleted.

When the filtering rule includes: a rule for a bright spot, 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:

Acquiring color information and texture information of the first data object included in the first data object combination and color information and texture information of at least one second data object combined with the first data object;

Performing a bright spot analysis on the color information of the first data object and the color information of the at least one second data object to obtain a first analysis result related to the bright spot;

Performing a bright spot analysis on the texture information of the first data object and the texture information of the at least one matching data object to obtain a second analysis result related to the bright spot;

It is determined whether the first analysis result and the second analysis result meet the rule for bright spots.

Correspondingly, the above 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:

When it is obtained according to the first analysis result and the second analysis result that the number of bright spots is greater than a second threshold, the first data object combination is deleted.

Based on the above content, the technical solution provided by this embodiment can be simply summarized as: a process of data object combination construction-filtering-sorting. For example, in the e-commerce scene of shoes, clothing and bags, 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. Among them, 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. In the filtering part, 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. Finally, you can also use the online recall + filter sorting search framework to support fast calculations and return results to meet real-time requirements; after experimental testing, it can produce matching products on a product set with more than 200 categories and balanced categories. The coverage rate exceeds 99%, and the matching satisfaction rate exceeds 80%.

Data object composition construction part

In 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. In the example shown in Figure 2, the user requested a dress. As shown in Figure 3, the dress can be empty or jacket for the top part, and empty or trousers for the bottom part. Shoes and bags are necessary. Then there are 2X2=4 kinds of rules to choose from, namely dress+shoes+bag combination rules 1, dress+coat+shoes+bag combination rules 2, dress+trousers+shoes+bag combination rules 3. dress+coat+trousers +Shoes+bag combination rule 4. Through index control, 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.

When 200 items of a certain type are recalled from the candidate product pool, such as shoes, they will be sorted according to their popularity points + shelf time to form a data object set 3 of shoes, and more popular and newer products can be given priority to participate Target product (such as dress) combination.

Next, you can use the four combination rules shown in Figure 2 in turn to produce a combination of data objects. For example, first use the "+shoes+bag" rule (i.e. combination rule 1), each use the first item in the shoe and bag queue, and combine the user's existing dress items to form a (dress+shoes+bag) 3-piece set Candidate; then, using the second "+coat+shoes+bag" rule (ie combination rule 2), take the first product from the jacket queue and the second product from the shoe and bag queues to form ( The same dress but combined with a jacket, new shoes and bag) 4-piece set candidates are passed to subsequent modules. By analogy, stop when the output candidates meet the quantity requirement (for example, 200).

Filter part

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. During specific implementation, 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.

In specific implementation, the filtering rules for styles can be simply understood as the style matching relationship represented in the table shown in 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. . Grid

with

Indicates a style that can be worn at the same time, in the grid

Indicates styles that are not suitable for wearing at the same time, such as "sweater" + "suit", "T-shirt" + "down pants", etc. In essence, 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. For example, 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 .

Generally, the color information of each product will contain 1 to 3 colors, and the texture information will contain 1 to 3 texture tags, as follows:

Commodity A:

Color information: (Color1,AreaC1), (Color2,AreaC2)...

Texture information: (Texture1,AreaT1), (Texture2,AreaT2)...

Among them, Color is a color label, such as carmine, and the corresponding Area is the proportion of the area of the color label in the product, such as 40%; similarly, Texture is a texture label, such as polka dots, and 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 condition for a color to be defined as the main color is that the area of the color label is Area>=30%, and the color in the HSV space S>=0.14 (otherwise it is black and white gray, black and white gray does not occupy the number of main colors). 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 condition for a color to be defined as a bright spot is that the color in the HSV space has S>=0.72 and V>=0.87 (very bright); the condition for a texture to be defined as a bright spot is that the area of the texture>=80% (Large area texture). 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.

After the above process, the remaining data object combinations that are not filtered out are examples of clothing matching that can be referenced by the user.

Sort part

Usually, after the above-mentioned filtering process, the remaining half can be provided to users as examples of clothing matching. Before being provided to users, these clothing matching examples can be sorted to determine the final display order for users. Usually this step is to further optimize the click-through rate of the match. For specific implementation, 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:

201. Acquire a second data object of at least one type different from the type to which the first data object belongs.

202. According to 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.

203. When the at least one second data object is matched with the first data object, provide the at least one second data object to the user.

In the above 201, 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. In the clothing e-commerce scenario, 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.

In the above 202, in a service application scenario, 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. Correspondingly, this step 202 may include at least one of the following implementation steps:

2021. 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;

2022. Determine the number of dominant colors according to the color information of the first data object and the color information of at least one second data object; determine the first data object and the at least one second data object according to the number of dominant colors Whether the data objects match;

2023. Determine, according to the color information of the first data object and the color information of the at least one second data object, 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 The texture information of the second 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 are Match.

The condition for a color to be defined as a bright spot is: the color in the HSV space has S>=0.72 and V>=0.87 (very bright); the condition for a texture to be defined as a bright spot is that the area of the texture>=80% (Large area texture). If, according to the first determination result and the second determination result, it is found that the bright spot combined by the first data object and the at least one second data object is not greater than 1, then the first data object is matched with the at least one second data object.

It should be noted here that the specific implementation of the foregoing 2021-2023 can refer to the corresponding content in the foregoing embodiment, which is not repeated here.

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. In addition, through actual results, 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. For example, a user selects a dress through a client application (APP), and adds the selected dress to a shopping cart. After 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. For example, in a feasible solution, 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. As shown in Figure 6, 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. In addition, through actual results, 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%.

In the foregoing clothing matching information processing system, the specific implementation methods of each component such as the client and the server will be described in detail in the following embodiments.

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. As shown in Figure 7, the method includes:

401. In response to an operation performed by a user on the first data object, send request information to a server.

402. Receive a clothing matching instance fed back by the server for the first data object.

403. Provide the clothing matching example to the user.

Wherein, 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.

In the above 401, 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. In addition, through actual results, 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%.

What needs to be explained here is that for the process of generating the clothing matching example, please refer to the relevant content in the foregoing embodiment, which will not be repeated here.

Further, the above step 403 "provide the clothing matching instance to the user" may include at least one of the following:

Display the clothing matching examples;

Based on the clothing matching example, display a virtual try-on image on the virtual model;

Acquire a photo of the user, and generate an image of the user for a virtual try-on based on the photo.

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. As shown in Figure 8, the method includes:

501. Acquire request information sent by a client for the first data object.

502. Extract 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 to which the first data object belongs, to generate multiple data object combinations.

503. Feed back the data object combination that meets the matching rule among the multiple data object combinations to the client as a clothing matching instance.

For the specific implementation of the foregoing steps 501 to 502, refer to the relevant content in the foregoing embodiment, and details are not described herein again.

Further, in addition to the above steps, the method provided in this embodiment can also implement other steps described in the above embodiments. For details, please refer to the descriptions 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 In addition, through actual results, 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. As shown in FIG. 9, the data object processing device includes: an acquisition module 11, a combination module 12 and a selection module 13. Wherein, 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 In addition, through actual results, 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%.

Further, the combination module 12 may be specifically used for:

Get at least one combination rule;

Using the at least one combination rule, 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.

Further, 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:

Acquiring the current sorting sequence number Ni corresponding to each type of data object set in the at least two types of data object sets, wherein the i values of different types of data object sets are different;

From the set of all data objects specified by the first combination rule, obtain the data objects ranked as Ni as the second data object and combine the first data object into a data object combination, and combine all the data objects specified by the first combination rule The corresponding Ni of each data object set is updated to Ni+1;

From the set of all data objects specified by the second combination rule, obtain the data objects ranked as Ni as the second data object and combine the first data object into a data object combination, and combine the data objects specified by the second combination rule The corresponding Ni of all data object sets is updated to Ni+1;

Repeat the above steps until the number of different combination instances generated meets the preset number requirements.

Further, the selection module 13 is also used for:

Acquiring attributes of data objects included in each data object combination in the multiple data object combinations;

Using a filter rule for at least one attribute item in the attributes to determine whether the attribute combination of the data object contained in each data object combination in the multiple data object combinations meets the filter rule;

The data object combination whose attribute combination of the data object contained in the multiple data object combination meets the filtering rule is deleted.

Further, 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.

Further, the filtering rules include: style-specific rules; correspondingly, the selection module 13 is also used for:

Acquiring the style of the first data object included in the first data object combination and the style of at least one second data object combined with the first data object;

It is determined whether the combination of the style of the first data object and the style of each second data object in the at least one second data object complies with the style-specific rule.

Still further, 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.

Further, the filtering rules include: rules for colors; and the selection module 13 is also used for:

Acquiring the color information of the first data object included in the first data object combination and the color information of at least one second data object combined with the first data object;

Based on the color information of the first data object and the color information of the at least one second data object, determining the number of main colors that meet the main color condition;

It is determined whether the number of main colors meets the color-specific rule.

Still further, 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.

Further, the filtering rules include: rules for bright spots; and the selection module 13 is also used for:

Acquiring color information and texture information of the first data object included in the first data object combination and color information and texture information of at least one second data object combined with the first data object;

Performing a bright spot analysis on the color information of the first data object and the color information of the at least one second data object to obtain a first analysis result related to the bright spot;

Performing a bright spot analysis on the texture information of the first data object and the texture information of the at least one matching data object to obtain a second analysis result related to the bright spot;

It is determined whether the first analysis result and the second analysis result meet the rule for bright spots.

Still further, the selection module 13 is also used for:

When it is obtained according to the first analysis result and the second analysis result that the number of bright spots is greater than a second threshold, the first data object combination is deleted.

Further, the acquisition module is also used for:

Acquiring the attributes of each data object in the first type data object pool;

Sort the data objects in the first type data object pool according to the attributes of each data object in the first type data object pool;

A plurality of data objects in the first order are selected to form the first type of data object set.

Further, 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.

What needs to be explained here is that the apparatus for processing clothing matching information provided in the foregoing embodiment can implement the technical solutions described in the foregoing method embodiments. For the specific implementation principles of 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. As shown in FIG. 10, the data object processing device includes: an acquisition module 21, a determination module 22, and a provision module 23. Wherein, 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. In addition, through actual results, 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%.

Further, in a clothing application scenario, 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 style of the first data object and the style of any one of the at least one second data object, determining whether the first data object and the at least one second data object match; and/or

Determining the number of dominant colors according to the color information of the first data object and the color information of at least one second data object; and/or

According to the number of the main colors, determine whether the first data object and the at least one second data object match; and/or

According to 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.

What needs to be explained here is that the data object processing device provided in the foregoing embodiment can implement the technical solutions described in the foregoing method embodiments. For the specific implementation principles of the foregoing modules or units, please refer to the corresponding content in the foregoing method embodiments. I won't repeat it here.

FIG. 11 shows a schematic structural diagram of an apparatus for processing clothing matching information provided by an embodiment of the present application. As shown in FIG. 11, 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. Wherein, 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.

Further, the output module is also used for:

Show the example of the outfit; and/or

Based on the clothing matching example, display a virtual try-on image on the virtual model; and/or

Acquire a photo of the user, and generate an image of the user for a virtual try-on based on the photo.

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. In addition, through actual results, 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%.

What needs to be explained here is that the apparatus for processing clothing matching information provided in the foregoing embodiment can implement the technical solutions described in the foregoing method embodiments. For the specific implementation principles of 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. As shown in FIG. 12, 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. In addition, through actual results, 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%.

What needs to be explained here is that the apparatus for processing clothing matching information provided in the foregoing embodiment can implement the technical solutions described in the foregoing method embodiments. For the specific implementation principles of 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.

The processor 52 is coupled with the memory 51, and is configured to execute the program stored in the memory 51 for:

Acquiring a data object set of at least one type different from the type to which the first data object belongs;

Extracting a second data object to be combined with the first data object from the at least one type of data object set to generate a plurality of data object combinations;

From the multiple data object combinations, a data object combination that meets the matching rules is selected as a clothing matching example that can be referenced by the user.

Wherein, when the 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.

Further, as shown in FIG. 13, 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:

Acquiring a second data object of at least one type different from the type to which the first data object belongs;

Determine whether the at least one second data object is matched with the first data object according to the attributes of the first data object and the attributes of the at least one second data object;

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.

Wherein, when the processor executes the program in the memory, in addition to the above functions, other functions may also be implemented. For details, please refer to the description of the previous embodiments.

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. Wherein, the processor is coupled with the memory, and is configured to execute the program stored in the memory for:

In response to the user's operation on the first data object, sending request information to the server;

Receiving a clothing matching instance fed back by the server for the first data object;

Providing the clothing matching example to the user;

Wherein, 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.

Wherein, when the processor executes the program in the memory, in addition to the above functions, other functions may also be implemented. For details, please refer to the description of the previous embodiments.

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. Wherein, the processor is coupled with the memory, and is configured to execute the program stored in the memory for:

Acquiring request information sent by the client for the first data object;

Extracting 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 to which the first data object belongs to generate multiple data object combinations;

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.

Wherein, when the processor executes the program in the memory, in addition to the above functions, other functions may also be implemented. For details, please refer to the description of the previous embodiments.

Correspondingly, 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.

In addition, 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.

Through the description of the above implementation manners, those skilled in the art can clearly understand that each implementation manner can be implemented by software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution essentially or the part that contributes to the existing technology can be embodied in the form of a software product. 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.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the application, not to limit them; although the application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand: The technical solutions recorded in the foregoing embodiments are modified, or some of the technical features thereof are equivalently replaced; these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims (23)

1. A method for processing clothing matching information, characterized in that it includes:

   Acquiring a data object set of at least one type different from the type to which the first data object belongs;

   Extracting a second data object to be combined with the first data object from the at least one type of data object set to generate a plurality of data object combinations;

   From the multiple data object combinations, a data object combination that meets the matching rules is selected as an example of clothing matching for the user's reference.
2. The method according to claim 1, wherein extracting a second data object to be combined with the first data object from the set of data objects of the at least one type to generate a plurality of data object combinations comprises:

   Get at least one combination rule;

   Using the at least one combination rule, 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.
3. The method according to claim 2, wherein the at least one combination rule comprises: a first combination rule and a second combination rule; and

   Using the first combination rule and the second combination rule, one data object is selected as the second data object and the first data object from a partial data object set or all data object sets in at least two types of data object sets. Data objects are combined into data object combinations; until the number of different data object combinations generated meets the preset number requirements, including:

   Acquiring the current sorting sequence number Ni corresponding to each type of data object set in the at least two types of data object sets, wherein the i values of different types of data object sets are different;

   From the set of all data objects specified by the first combination rule, obtain the data objects ranked as Ni as the second data object and combine the first data object into a data object combination, and combine all the data objects specified by the first combination rule The corresponding Ni of each data object set is updated to Ni+1;

   From the set of all data objects specified by the second combination rule, obtain the data objects ranked as Ni as the second data object and combine the first data object into a data object combination, and combine the data objects specified by the second combination rule The corresponding Ni of all data object sets is updated to Ni+1;

   Repeat the above steps until the number of different combination instances generated meets the preset number requirements.
4. The method according to any one of claims 1 to 3, characterized in that, from the plurality of data object combinations, selecting a data object combination that meets matching rules as an example of clothing matching that can be referenced by a user includes:

   Acquiring attributes of data objects included in each data object combination in the multiple data object combinations;

   Using a filter rule for at least one attribute item in the attributes to determine whether the attribute combination of the data object contained in each data object combination in the multiple data object combinations meets the filter rule;

   The data object combination whose attribute combination of the data object contained in the multiple data object combination meets the filtering rule is deleted.
5. The method according to claim 4, wherein the attributes of the data object include at least one of the following attribute items: type, style, color information, texture information, sales record, purchase rate, click rate, and shelf time.
6. The method according to claim 5, wherein the filtering rules comprise: style-specific rules; and

   Using the style-specific rules to determine whether the attribute combination of the data objects contained in the first data object combination among the multiple data object combinations meets the style-specific rules includes:

   Acquiring the style of the first data object included in the first data object combination and the style of at least one second data object combined with the first data object;

   It is determined whether the combination of the style of the first data object and the style of each second data object in the at least one second data object complies with the style-specific rule.
7. The method according to claim 6, wherein deleting the data object combination whose attribute combination of the data object contained in the multiple data object combination meets the filter rule comprises:

   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 meets the style-specific rule, the first data object combination is deleted.
8. The method according to claim 5, wherein the filtering rules comprise: rules for colors; and

   Using the color-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 conforms to the style-specific rule includes:

   Acquiring the color information of the first data object included in the first data object combination and the color information of at least one second data object combined with the first data object;

   Based on the color information of the first data object and the color information of the at least one second data object, determining the number of main colors that meet the main color condition;

   It is determined whether the number of main colors meets the color-specific rule.
9. The method according to claim 8, wherein deleting the data object combination whose attribute combination of the data object contained in the multiple data object combination meets the filtering rule comprises:

   When the number of dominant colors is greater than a first threshold, the first data object combination is deleted.
10. The method according to claim 5, wherein the filtering rules comprise: rules for bright spots; and

    Using the rule for the bright spot 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 rule for the bright spot, including:

    Acquiring color information and texture information of the first data object included in the first data object combination and color information and texture information of at least one second data object combined with the first data object;

    Performing a bright spot analysis on the color information of the first data object and the color information of the at least one second data object to obtain a first analysis result related to the bright spot;

    Performing a bright spot analysis on the texture information of the first data object and the texture information of the at least one matching data object to obtain a second analysis result related to the bright spot;

    It is determined whether the first analysis result and the second analysis result meet the rule for bright spots.
11. The method according to claim 8, wherein deleting the data object combination whose attribute combination of the data object contained in the multiple data object combination meets the filtering rule comprises:

    According to the first analysis result and the second analysis result, when the number of bright spots is greater than the second threshold, the first data object combination is deleted.
12. The method according to any one of claims 1 to 3, wherein obtaining a first type of data object set that is different from the type to which the first data object belongs comprises:

    Acquiring the attributes of each data object in the first type data object pool;

    Sort the data objects in the first type data object pool according to the attributes of each data object in the first type data object pool;

    A plurality of data objects in the first order are selected to form the first type of data object set.
13. The method according to any one of claims 1 to 3, characterized in that, in the case where there are multiple instances of clothing matching available for the user's reference, the method further comprises:

    Sort multiple instances of clothing collocation;

    According to the sorting result, multiple instances of clothing matching are provided to the user.
14. A data object processing method, characterized in that it comprises:

    Acquiring a second data object of at least one type different from the type to which the first data object belongs;

    Determine whether the at least one second data object is matched with the first data object according to the attributes of the first data object and the attributes of the at least one second data object;

    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.
15. The method according to claim 14, wherein the attributes include at least one of the following: location, style, color information, texture information, sales records, additional purchase rate, click rate, and shelf time; and

    According to the attribute of the first data object and the attribute of the at least one second data object, determining whether the at least one second data object is matched with the first data object includes at least one of the following:

    Determining whether the style of the first data object matches the style of any one of the at least one second data object and the at least one second data object;

    Determine the number of main colors according to the color information of the first data object and the color information of at least one second data object; determine the first data object and the at least one second data object according to the number of the main colors Whether to match

    According to 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.
16. A method for processing clothing matching information, characterized in that it includes:

    In response to the user's operation on the first data object, sending request information to the server;

    Receiving a clothing matching instance fed back by the server for the first data object;

    Providing the clothing matching example to the user;

    Wherein, 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.
17. The method according to claim 16, wherein providing the clothing matching instance to the user includes at least one of the following:

    Display the clothing matching examples;

    Based on the clothing matching example, display a virtual try-on image on the virtual model;

    Acquire a photo of the user, and generate an image of the user for a virtual try-on based on the photo.
18. A method for processing clothing matching information, characterized in that it includes:

    Acquiring request information sent by the client for the first data object;

    Extracting 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 to which the first data object belongs to generate multiple data object combinations;

    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.
19. A processing system for clothing matching information, characterized in that it includes:

    The client 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 Said 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.
20. An electronic device characterized by comprising 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:

    Acquiring a data object set of at least one type different from the type to which the first data object belongs;

    Extracting a second data object to be combined with the first data object from the at least one type of data object set to generate a plurality of data object combinations;

    From the multiple data object combinations, a data object combination that meets the matching rules is selected as a clothing matching example that can be referenced by the user.
21. An electronic device characterized by comprising 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:

    Acquiring a second data object of at least one type different from the type to which the first data object belongs;

    Determine whether the at least one second data object is matched with the first data object according to the attributes of the first data object and the attributes of the at least one second data object;

    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.
22. A client device, which is characterized by comprising 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:

    In response to the user's operation on the first data object, sending request information to the server;

    Receiving a clothing matching instance fed back by the server for the first data object;

    Providing the clothing matching example to the user;

    Wherein, 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.
23. A server device, characterized in that it 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:

    Acquiring request information sent by the client for the first data object;

    Extracting 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 to which the first data object belongs to generate multiple data object combinations;

    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.

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