CN109389450B

CN109389450B - Method and system for associating attribute information and generating data set

Info

Publication number: CN109389450B
Application number: CN201710671686.0A
Authority: CN
Inventors: 周嘉源
Original assignee: Alibaba Group Holding Ltd
Current assignee: Alibaba Group Holding Ltd
Priority date: 2017-08-08
Filing date: 2017-08-08
Publication date: 2022-04-19
Anticipated expiration: 2037-08-08
Also published as: CN109389450A

Abstract

The application relates to the technical field of internet, in particular to a method and a system for associating attribute information and generating a data set, which are used for solving the problem that the attribute information of an object on one network publishing platform cannot be associated with the attribute information of objects on other network publishing platforms in the prior art. Determining a second historical data set of a second platform containing second attribute information, wherein the second historical data set is associated with a first historical data set containing first attribute information; and determining second attribute information associated with the first attribute information according to the first historical data set and a second historical data set associated with the first historical data set. Since the second attribute information associated with the first attribute information can be determined from the first historical data set and the second historical data set associated with the first historical data set, the first attribute information of the first platform object can be associated with the second attribute information of the second platform object.

Description

Method and system for associating attribute information and generating data set

Technical Field

The present application relates to the field of internet technologies, and in particular, to a method and a system for associating attribute information and generating a data set.

Background

The network publishing platform is a platform for a publisher to publish own products through a network, and a user to browse the products through the network and interact through the network.

When a publisher publishes a product on a network publishing platform, the publisher configures product information for the product. According to the product information, a user can interact with the website publishing platform aiming at a product.

At present, a product may be published on a plurality of different web publishing platforms, and since a template of product information corresponding to each web publishing platform may be different, if some users need to perform an interaction action on one product on other web publishing platforms according to interaction information on the web publishing platforms, the users may only manually interact on the other web publishing platforms.

At present, because the templates of the product information corresponding to each network publishing platform may be different, the attribute information of different network publishing platforms is different, and the attribute information of an object on one network publishing platform cannot be associated with the attribute information of objects on other network publishing platforms.

Disclosure of Invention

The application provides a method and a system for associating attribute information and generating a data set, which are used for solving the problem that the attribute information of an object on one network publishing platform cannot be associated with the attribute information of objects on other network publishing platforms in the prior art.

The method for associating attribute information provided by the embodiment of the application comprises the following steps:

determining a first historical data set of a first platform containing first attribute information for first attribute information of a first target object in the first platform;

determining a second historical data set of a second platform containing second attribute information associated with the first historical data set;

determining the second attribute information associated with the first attribute information according to the first historical data set and the second historical data set associated with the first historical data set.

The system for associating attribute information provided by the embodiment of the application comprises:

the system comprises a set determining module, a first storage module and a second storage module, wherein the set determining module is used for determining a first historical data set of a first platform containing first attribute information aiming at the first attribute information of a first target object in the first platform;

a set association module for determining a second historical data set of a second platform associated with the first historical data set that contains second attribute information;

an information association module, configured to determine, according to the first historical data set and the second historical data set associated with the first historical data set, the second attribute information associated with the first attribute information.

The method for generating the data set provided by the embodiment of the application comprises the following steps:

determining second attribute information associated with first attribute information in a first data set according to the association relationship between the first attribute information and the second attribute information aiming at the first data set in a first platform;

generating a second data set in a second platform according to the determined second attribute information;

wherein the incidence relation is determined according to the following mode:

The system for generating a data set provided by the embodiment of the application comprises:

the processing module is used for determining second attribute information associated with the first attribute information in the first data set according to the association relationship between the first attribute information and the second attribute information aiming at the first data set in the first platform;

the generating module is used for generating a second data set in a second platform according to the determined second attribute information;

wherein the incidence relation is determined according to the following mode:

Determining a first historical data set of a first platform containing the first attribute information, and determining a second historical data set of a second platform containing second attribute information, wherein the second historical data set is associated with the first historical data set; determining the second attribute information associated with the first attribute information according to the first historical data set and the second historical data set associated with the first historical data set. Since the second attribute information associated with the first attribute information can be determined from the first historical data set and the second historical data set associated with the first historical data set, the first attribute information of the first platform object can be associated with the second attribute information of the second platform object.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.

Fig. 1 is a schematic flowchart of a method for associating attribute information according to an embodiment of the present application;

FIG. 2 is a schematic diagram illustrating a process of establishing a mapping relationship according to an embodiment of the present application;

FIG. 3 is a schematic diagram illustrating a process of adding similar objects for association according to an embodiment of the present application;

FIG. 4 is a schematic diagram illustrating a mapping relationship between a downstream commodity and an upstream commodity according to an embodiment of the present application;

FIG. 5 is a schematic diagram of similar merchandise association according to an embodiment of the present application;

FIG. 6 is a schematic diagram illustrating a mapping relationship between a downstream commodity and an upstream commodity added with a similar commodity in an embodiment of the present application;

FIG. 7 is a diagram illustrating an associated object according to an embodiment of the present application;

FIG. 8 is a flowchart illustrating a complete method for establishing a mapping relationship according to an embodiment of the present application;

FIG. 9 is a schematic structural diagram of a system for establishing a mapping relationship according to an embodiment of the present application;

FIG. 10 is a flowchart illustrating a method for generating a data set according to an embodiment of the present application;

fig. 11 is a schematic structural diagram of a system for generating a data set according to an embodiment of the present application.

Detailed Description

The embodiment of the application can be applied to a scene with a plurality of platforms with incidence relations. Such as a factoring scenario.

If the method is applied to a sales environment, any object capable of conducting transaction can be taken as an object of the embodiment of the application, such as goods, services and the like.

The first platform may be a downstream platform (e.g., a retail platform such as Taobao, Racket, Jingdong, Sunning, Amazon, ebay, etc.) and the second platform may be an upstream platform (e.g., a wholesale platform such as 1688, Excellent wholesale network, Smart network, etc.).

If a customer places an order to purchase an item on the downstream platform, a downstream user selling the item needs to place an order on the upstream platform to have the upstream user ship to the customer.

The order of the downstream platform is an order generated according to the commodity purchased by the user; the order of the upstream platform is an order for sale generated according to the commodity order purchased by the user.

For example, a user purchases a jacket at a small B business on a first platform, which generates a jacket order including size, purchase quantity, price, shipping address, etc.; since the small B merchant is the jacket of the large B merchant who has sold the second platform, an order for the second platform is generated according to the order on the first platform, and finally the large B merchant sends the jacket to the user according to the order on the second platform.

The target attribute information of the embodiment of the present application may be information of the product purchased by the buyer placing the order on the downstream platform (i.e., the first platform).

The attribute information of the object in the embodiment of the application includes attribute content, and further includes attribute type. The attribute type is the expression of the information of the object itself, and the attribute content is the information of the object itself. For example, when the object is red, the color is attribute type, and red is attribute content.

Wherein, corresponding attribute information is different for different scenes, different platforms and different objects.

For example, the method is applied to a sales promotion scene, and the object is clothes with red color and short sleeve type. The attribute information of the clothes of the platform A is (clothes), (color: colorful red) and (clothes type: short sleeve); the attribute information of the clothes of the platform B is (clothes), (color: bright red) and (clothes type: half sleeve).

Different applications the meaning of the historical data set of the embodiments of the present application also differs. Taking a commission scenario as an example, the historical data set of the embodiment of the present application may be a historical order.

In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be described in further detail with reference to the accompanying drawings, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

As shown in fig. 1, a method for associating attribute information in an embodiment of the present application includes:

step 100, aiming at first attribute information of a first target object in a first platform, determining a first historical data set of the first platform containing the first attribute information;

step 101, determining a second historical data set of a second platform which is associated with the first historical data set and contains second attribute information;

step 102, determining the second attribute information associated with the first attribute information according to the first historical data set and the second historical data set associated with the first historical data set.

In an implementation, the first attribute information for the first target object in the first platform may be any one of the first attribute information for the first target object in the first platform, or may be at least one of the first attribute information for the first target object in the first platform.

The first target object in the embodiment of the present application is an object on a first platform, the first attribute information is attribute information of the object on the first platform, and the first historical data set is a first historical data set of the first platform;

the second attribute information in the embodiment of the present application is attribute information of an object on the second platform, and the second history data set is a second history data set of the second platform.

In an implementation, the second attribute information associated with each first attribute information of each first target object may be determined separately.

The object on each platform may have one or more attribute information. Taking the application to a sales promotion scene as an example, the attribute information of a piece of clothes may be (colorful red, number L); (dazzle color red, number X); (sky blue, No. L); (sky blue, number X) four attribute information.

One first attribute information may be associated with at least one second attribute information; accordingly, one second attribute information may be associated with at least one first attribute information. After the first attribute information and the second attribute information are determined to be associated each time, the associated first attribute information and the associated second attribute information can be placed in the attribute information mapping relation.

The method for establishing the mapping relation in the embodiment of the application can be divided into two steps: 1. establishing historical data set association; 2. the attribute information association is established, which may be specifically referred to in fig. 2.

When historical data set association is established, orders of each platform are determined from historical transaction data by taking the platform as a dimension respectively for different platforms, for example, transaction data of a Taobao platform and a 1688 platform are two different dimensions.

How to determine the associated first attribute information and second attribute information is described in detail below.

First attribute information of a first target object in the first platform is determined, and the first attribute information may be referred to as attribute information to be associated.

And determining a first historical data set containing attribute information to be associated from all the first historical data sets of the first platform.

There may be one or more first sets of historical data containing attribute information to be associated.

Then, for each determined first historical data set containing the attribute information to be associated, a second historical data set associated with the first historical data set needs to be found.

And finally, determining the second attribute information associated with the first attribute information according to the first historical data set and the second historical data set associated with the first historical data set.

Taking the factoring scenario as an example, the first set of historical data may be an order. Because each order contains the commodity purchased by the user and the attribute information such as the color, the model and the style of the commodity, the order containing the attribute information to be associated can be directly searched from all historical orders of the first platform.

In practice, a duration may be set, such as the last half year, and a search may be made from historical orders for the last half year. Of course, the longer the set duration is, the larger the data size of the historical order is, the more comprehensive the obtained association relationship is, and the relative data size and the load of the equipment are also larger. The time period can be set according to specific needs.

After finding a first historical data set containing attribute information to be associated, a second historical data set associated can be determined.

Optionally, a second historical data set containing key information in the first historical data set is searched from a second historical data set of a second platform;

and taking the searched second historical data set as the second historical data set associated with the first historical data set.

The content of the key information is different for different application scenarios.

Taking application in a sales environment as an example, the historical data sets are orders, in the sales environment, after a user purchases a commodity on a downstream platform (i.e., a first platform), the downstream platform generates a downstream order (i.e., the first historical data set), a downstream seller generates an upstream order (i.e., a second historical data set) corresponding to the downstream order through an upstream platform (i.e., a second platform), and the upstream seller directly delivers the commodity to the user. Based on this, it can be known that the logistics information (including the courier order number, the shipping address, the consignee and the time of shipment, etc.) of the downstream order and the upstream order are the same. Downstream orders can be associated with orders placed thereon through logistics information. The key information here may be logistics information.

In the brokering scenario, if there are multiple items in a downstream order, the downstream seller may place an order for the upstream seller or multiple upstream sellers.

If an upstream seller is found to place an order, a downstream order corresponds to an upstream order;

if multiple upstream sellers are found to place orders, one downstream order will correspond to multiple upstream orders.

In practice, therefore, in order to facilitate the need for the associated second historical data set, the first historical data set of the first platform containing the first attribute information may be determined in units of logistics information.

That is, the logistics information in a first historical data set of a first platform containing the first attribute information is unique. Therefore, the logistics information that two first historical data sets have the same logistics information can be prevented, and the logistics information corresponding to one first historical data set can be prevented.

For example, if a downstream order includes a commodity a and a commodity B, and the downstream order corresponds to a plurality of logistics information, which indicates that the commodity a and the commodity B respectively correspond to different logistics information, the downstream order may be split to obtain two downstream orders (which may also be referred to as downstream sub-orders), where each downstream sub-order includes a commodity and corresponds to one logistics information;

if the downstream order corresponds to one piece of logistics information, it is indicated that the commodity A and the commodity B correspond to the same logistics information, and the downstream order does not need to be split.

After order association is performed, attribute information association may be performed.

Optionally, selecting a first historical data set from the determined first historical data sets;

and determining second attribute information associated with the first attribute information according to the number of the first attribute information included in the selected first historical data set and the number of the second attribute information included in a second historical data set associated with the selected first historical data set.

The number of the first attribute information included in the first history data set is different from the number of the second attribute information included in the second history data set, and the manner of determining the second attribute information associated with the first attribute information is also different, and the following description is made with respect to the different numbers respectively.

The number of the first attribute information included in the first historical data set is the same as the number of the second attribute information included in the associated second historical data set, and the number of the first attribute information included in the selected first historical data set is not larger than a threshold value.

Optionally, when determining the second attribute information associated with the first attribute information, it is determined that the first attribute information included in the selected first historical data set is associated with the second attribute information included in the associated second historical data set.

The threshold value can be set as needed, for example, 1.

Taking a sales promotion scenario as an example, in the sales promotion scenario, the downstream seller generates an upstream order corresponding to the downstream order through the upstream platform, so the quantities of the attribute information included in the downstream order and the upstream order are the same.

The set threshold is illustrated as 1.

Assuming that one first history data set selected from the determined first history data sets and a second history data set associated with the selected first history data set are as shown in table 1:

platform A	Platform B
		Order a	Order b
Jacket: blue, number L	Shirt: sky blue, big code

TABLE 1

In table 1, there are order a for platform a and order B for platform B, each of which includes attribute information, and since order a and order B are related orders, order a (jacket: blue, number L) can be related to order B (shirt: sky blue, big code).

And secondly, the number of the first attribute information included in the first historical data set is the same as the number of the second attribute information included in the associated second historical data set, and the number of the first attribute information included in the selected first historical data set is larger than a threshold value.

Optionally, the selected first historical data set is used as a first screening historical data set, and the associated second historical data set is used as a second screening historical data set;

continuing to select a first historical data set, removing attribute information in the first filtered historical data set, which is different from the most recently selected first historical data set, and removing attribute information in the second filtered historical data set, which is different from the second historical data set associated with the most recently selected first historical data set;

judging whether the quantity of first attribute information included in the first screening historical data set is not greater than a threshold value, if so, determining that the first attribute information included in the first screening historical data set is associated with second attribute information included in the second screening historical data set;

otherwise, returning to the step of continuously selecting a first historical data set.

The threshold value can be set as needed, for example, 1.

If the number of the first attribute information in the first historical data set is larger than a threshold value, continuing to select the first historical data set, and screening the first attribute information in the first selected first historical data set according to the later selected first historical data set until the first attribute information in the first historical data set is not larger than the threshold value; and similarly, screening the second historical data set until the second attribute information in the second historical data set is not greater than the threshold value.

And finally, performing attribute information association as the first attribute information in the first historical data set and the second attribute information in the second historical data set are not more than the threshold value.

The set threshold is illustrated as 1.

Assume that one first historical data set selected from the determined first historical data sets and a second historical data set associated with the selected first historical data set are as shown in table 2:

TABLE 2

In table 2, there are order a for platform a and order B for platform B, where order a includes two first attribute information (jacket: blue, L number) and (pants: army green, S number), and order B includes two second attribute information (shirt: sky blue, big size) and (shoe: black, 43 number).

Since the number of the first attribute information included in the selected first history data set is greater than the threshold value, the selection needs to be performed.

Here, the first history data set in table 2 is taken as the first filtering history data set, and the second history data set in table 2 is taken as the second filtering history data set.

The selection of the first set of historical data then continues from the determined unselected first set of historical data. Since the determined first sets of historical data all comprise the first data information of the first target object, which first set of historical data all comprises at least the first data information of the first target object is selected.

Assume that the first historical data set selected continuously and the second historical data set associated with the first historical data set selected continuously are as shown in table 3:

TABLE 3

In table 3, there are order c for platform a and order d for platform B, where order c includes two first attribute information (glove: blue stripe, average code) and (jacket: blue, No. L), and order d includes two second attribute information (sock: design pattern, average code) and (shirt: sky blue, big code).

And removing the first attribute information which is different from the order c in the order a. If the comparison shows that the first attribute information (trousers: army green and S number) in the order a is different from the order c, rejecting the first attribute information (trousers: army green and S number);

and removing second attribute information which is different from the order d in the order b. And if the comparison shows that the first attribute information (shoe: black, No. 43) in the order b is different from the order d, rejecting the order (shoe: black, No. 43). The rejected orders a and b are shown in table 4:

TABLE 4

In table 4, there are order a for platform a and order B for platform B, each of which includes attribute information, and since order a and order B are related orders, order a (jacket: blue, number L) can be related to order B (shirt: sky blue, big code).

If the number of attribute information included in the order in table 4 is still greater than the threshold value, the first historical data set is continuously selected for culling processing, and the specific process is similar to the above steps and will not be described repeatedly here.

According to the above, the mapping relationship between the first attribute information and the second attribute information can be established by the association of the history data sets.

Since not all mapping relationships can be established through the historical data set, in order to establish the mapping relationships more comprehensively, the embodiment of the present application may further continue to associate the attribute information by using the established mapping relationships.

Take the application in a sales environment as an example:

as shown in fig. 3, the mapping relationship in the embodiment of the present application includes attribute information associated with a history data set and attribute information associated with a similar object.

As shown in fig. 4, it is assumed that:

attribute information a of downstream article X1 is associated with attribute information a1 of upstream article Y1 and attribute information a2 of upstream article Y2;

attribute information B of the downstream article X2 is associated with attribute information B1 of the upstream article Y3;

the attribute information C of the downstream article X3 is associated with the attribute information C1 of the upstream article Y4.

As shown in fig. 5, assuming that attribute information a of downstream commodity X1 is associated with attribute information B of downstream commodity X2 and attribute information a of downstream commodity X1 is associated with attribute information C of downstream commodity X3, attribute information a of downstream commodity X1 further includes attribute information B1 of upstream commodity Y3 and attribute information C1 of upstream commodity Y4, as can be specifically seen in fig. 6.

How the mapping relationships are extended is described in detail below.

Optionally, after determining the second attribute information associated with the first attribute information, for the first attribute information of the second target object in the first platform, determining other objects on the first platform that are associated with the first target object;

determining first attribute information which is the same as the first attribute information of the second target object in the first attribute information of the other objects;

associating second attribute information associated with the determined first attribute information of the other object with the first attribute information of the second target object.

The association is made here through other objects associated with the platform.

The first attribute information for the second target object in the first platform may be any one of the first attribute information for the second target object in the first platform, or may be at least one of the first attribute information for the second target object in the first platform.

In implementation, the first target object and the second target object may be the same object or different objects.

There are many ways to determine other objects on the first platform that are related to the first target object, and the following are some:

1. and determining other objects which are related to the second target object on the first platform according to the picture in the first platform.

Since the pictures corresponding to the same first attribute information of the objects (such as similar objects) associated with the platform are not very different, other objects on the first platform that are associated with the second target object can be determined by the pictures in the first platform.

In implementation, the similarity of the pictures corresponding to the first attribute information of the two objects on the same platform can be determined through a picture similarity algorithm, and whether the two objects are similar or not is further judged.

There are many specific similarity algorithms, such as Scale-invariant feature transform (SIFT).

2. And determining other objects which are related to the second target object on the first platform according to the second attribute information which is related to the first attribute information of the second target object and the second attribute information which is related to the first attribute information of the other objects.

Here, the other objects on the first platform that are related to the second target object are determined by the second attribute information related to each first attribute information.

If the second attribute information associated with the first attribute information of each of the two objects of the platform is the same in the mapping relationship determined by using the historical data set, the two objects may be considered to be associated, and specifically, see fig. 7.

3. And determining other objects which are related to the first target object on the first platform according to the identification corresponding to the first attribute information of the first target object and the identifications corresponding to the first attribute information of the other objects.

The specific identification content may be different according to different application scenarios.

Taking the affiliate scenario as an example, the identifier may be a skid (Stock locating Unit ID).

One SKUID corresponds to one attribute, for example, trousers correspond to one SKUID, blue corresponds to one SKUID, and an L number corresponds to one SKUID. One attribute information includes at least one attribute, that is, a group of skiids corresponding to one attribute information includes at least one skiid.

Taking the sales scenario as an example, since the names of the attribute information in the commodities a and c on the same platform are different (for example, one is L and the other is large L), but a group of skids corresponding to the bottom layer are the same, object association can be performed through the skids.

The above methods may be applied to only one kind, or may be applied to a plurality of kinds.

It should be noted that the above-mentioned several ways are only examples, and any way capable of determining other objects on the first platform that are related to the first target object is applicable to the embodiment of the present application.

After determining the other objects associated with the second target object, the first attribute information, which is the same as the first attribute information of the second target object, in the first attribute information of the other objects may be further determined, and this may be determined by the sked.

And then determining second attribute information associated with the determined first attribute information of the other object according to the mapping relation between the first attribute information and the second attribute information, and associating the second attribute information with the first attribute information of the second target object.

Take a sales-for-stock scenario as an example:

table 5 is the first attribute information of the first platform item a;

table 6 is second attribute information of the second platform article b;

table 7 shows the first attribute information of the first platform product c.

Commodity	a
		Size measuring device	L
Size measuring device	M

TABLE 5

Commodity	b
		Size measuring device	User-defined big code
Size measuring device	User-defined small code

TABLE 6

Commodity	c
		Size measuring device	Small L
Size measuring device	Small M

TABLE 7

Table 8 shows the relationship between the first attribute information of the product a and the second attribute information of the product b determined by the mapping relationship.

Table 9 shows the relationship between the first attribute information of the product c and the second attribute information of the product b determined by the mapping relationship.

Commodity a	Article b
		L	User-defined big code

TABLE 8

Goods c	Article b
		Small M	User-defined small code

TABLE 9

From the SKUID it can be determined:

the first attribute information "M" of the article a is the same as the first attribute information "small M" of the article c;

the first attribute information "L" of the product a is the same as the first attribute information "small L" of the product c, and specifically, see table 10.

Commodity a	Goods c
		M	Small M
L	Small L

Watch 10

According to the above, it can be seen that:

since the first attribute information "small M" of the commodity c is associated with the second attribute information "custom small code" of the commodity b, the association between the first attribute information "M" of the commodity a and the second attribute information "custom small code" of the commodity b can be established, which can be referred to table 11;

since the first attribute information "L" of the commodity a is associated with the second attribute information "custom big code" of the commodity b, the association of the first attribute information "small M" of the commodity c with the second attribute information "custom big code" of the commodity b may be established, which may be referred to table 12.

Commodity a	Article b
		M	User-defined small code

TABLE 11

Goods c	Article b
		Small L	User-defined big code

TABLE 12

And finally, adding the obtained new mapping into the mapping relation so as to expand the mapping relation, thereby establishing more mapping relations between the first attribute information and the second attribute information.

As shown in fig. 8, the complete method for establishing a mapping relationship in the embodiment of the present application includes:

step 800, for any first attribute information of a first target object in a first platform, determining a first historical data set of the first platform containing the first attribute information.

Step 801, searching a second historical data set containing key information in the first historical data set from a second historical data set of a second platform.

Step 802, using the found second historical data set as the second historical data set associated with the first historical data set.

Step 803, selecting a first historical data set from the determined first historical data sets.

Step 804, judging whether the number of the first attribute information included in the selected first historical data set is not greater than a threshold value, if so, executing step 810; otherwise, step 805 is performed.

Step 805, the selected first historical data set is used as a first screening historical data set, and the associated second historical data set is used as a second screening historical data set.

Step 806, continue to select a first historical data set.

Step 807, removing attribute information in the first filtered set of history data that is different from the most recently selected first set of history data, and removing attribute information in the second filtered set of history data that is different from the second set of history data associated with the most recently selected first set of history data.

Step 808, judging whether the quantity of the first attribute information included in the first screening history data set is not greater than a threshold value, if so, executing step 809; otherwise, return to step 806.

Step 809 determines that the first attribute information included in the first filtered historical data set is associated with the second attribute information included in the second filtered historical data set.

Step 810, determining other objects related to the first target object on the first platform according to any one first attribute information of the second target object in the first platform.

Step 811, determining the first attribute information of the other objects, which is the same as the first attribute information of the second target object.

And step 812, associating the second attribute information associated with the determined first attribute information of the other object with the first attribute information of the second target object.

In implementation, the established mapping relationships may be updated periodically or in real-time through historical data sets, and may be augmented periodically or in real-time through associated objects. The updating and expanding processes have no necessary timing relationship.

Based on the same inventive concept, the embodiment of the present application further provides a system for establishing a mapping relationship, and as the principle of solving the problem of the system is similar to the method for establishing the mapping relationship in the embodiment of the present application, the implementation of the system may refer to the implementation of the method, and repeated details are not repeated.

As shown in fig. 9, the system for establishing a mapping relationship according to the embodiment of the present application includes:

a set determining module 900, configured to determine, for first attribute information of a first target object in a first platform, a first historical data set of the first platform that includes the first attribute information;

a set association module 901, configured to determine a second historical data set of a second platform that includes second attribute information and is associated with the first historical data set;

an information associating module 902, configured to determine the second attribute information associated with the first attribute information according to the first historical data set and the second historical data set associated with the first historical data set.

Optionally, the set association module 901 is specifically configured to:

searching a second historical data set containing key information in the first historical data set from a second historical data set of a second platform;

Optionally, the information association module 902 is specifically configured to:

selecting a first historical data set from the determined first historical data sets;

if the number of the first attribute information included in the selected first historical data set is not larger than a threshold value, determining that the first attribute information included in the selected first historical data set is associated with the second attribute information included in the associated second historical data set.

The threshold value can be set as needed, for example, 1.

The set threshold is illustrated as 1.

It is assumed that one first historical data set selected from the determined first historical data sets and a second historical data set associated with the selected first historical data set are as shown in table 1.

if the number of the first attribute information included in the selected first historical data set is larger than a threshold value, taking the selected first historical data set as a first screening historical data set, and taking the associated second historical data set as a second screening historical data set;

The threshold value can be set as needed, for example, 1.

The set threshold is illustrated as 1.

It is assumed that one first historical data set selected from the determined first historical data sets and a second historical data set associated with the selected first historical data set are as shown in table 2.

It is assumed that the first historical data set selected continuously and the second historical data set associated with the first historical data set selected continuously are as shown in table 3.

and removing second attribute information which is different from the order d in the order b. And if the comparison shows that the first attribute information (shoe: black, No. 43) in the order b is different from the order d, rejecting the order (shoe: black, No. 43). The culled orders a and b are shown in table 4.

Alternatively to this, the first and second parts may,

the information association module 902 is further configured to:

determining other objects which are related to a first target object on a first platform aiming at first attribute information of a second target object in the first platform;

There are many ways for the information association module 902 to determine other objects on the first platform that are associated with the first target object, which are listed as follows:

Taking the factoring scenario as an example, the identifier may be a skiid.

Take a sales-for-stock scenario as an example:

table 5 is the first attribute information of the first platform item a;

table 6 is second attribute information of the second platform article b;

table 7 shows the first attribute information of the first platform product c.

From the SKUID it can be determined:

According to the above, it can be seen that:

The modules in fig. 9 may be provided in one device, or may be provided in a plurality of devices. The functions of one module may be implemented by one device or by a plurality of devices.

After the mapping relationship between the first historical data set and the second historical data set is established, the mapping relationship can be used. There are many scenarios of use, one of which is listed below:

as shown in fig. 10, a method for generating a data set according to an embodiment of the present application includes:

step 1000, determining second attribute information associated with first attribute information in a first data set according to an association relationship between the first attribute information and the second attribute information for the first data set in a first platform;

1001, generating a second data set in a second platform according to the determined second attribute information;

wherein the incidence relation is determined according to the following mode:

Fig. 10 is a usage scenario in which only one association relationship between the first attribute information and the second attribute information is listed, and there are many usage scenarios of the mapping relationship, for example, recommendation information may be determined according to the mapping relationship.

Specifically, according to a mapping relationship between attribute information of a first object and attribute information of a second object, determining attribute information of the second object corresponding to the target attribute information, where the target attribute information is attribute information of an object in a first platform, the first object is an object in the first platform, and the second object is an object in the second platform;

selecting at least one attribute information meeting the recommendation strategy information from the determined attribute information of the second object according to the set recommendation strategy information;

and determining recommendation information corresponding to the target attribute information according to the selected attribute information.

Based on the same inventive concept, the embodiment of the present application further provides a system for generating a data set, and as the principle of solving the problem of the system is similar to the method for generating the data set in the embodiment of the present application, the implementation of the system may refer to the implementation of the method, and repeated details are not repeated.

As shown in fig. 11, a system for generating a data set according to an embodiment of the present application includes:

a processing module 1100, configured to determine, for a first data set in a first platform, second attribute information associated with first attribute information in the first data set according to an association relationship between the first attribute information and the second attribute information;

a generating module 1110, configured to generate a second data set in a second platform according to the determined second attribute information;

wherein the incidence relation is determined according to the following mode:

The modules in fig. 11 may be provided in one device, or may be provided in a plurality of devices. The functions of one module may be implemented by one device or by a plurality of devices.

The present application is described above with reference to block diagrams and/or flowchart illustrations of methods, apparatus (systems) and/or computer program products according to embodiments of the application. It will be understood that one block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, and/or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer and/or other programmable data processing apparatus, create means for implementing the functions/acts specified in the block diagrams and/or flowchart block or blocks.

Accordingly, the subject application may also be embodied in hardware and/or in software (including firmware, resident software, micro-code, etc.). Furthermore, the present application may take the form of a computer program product on a computer-usable or computer-readable storage medium having computer-usable or computer-readable program code embodied in the medium for use by or in connection with an instruction execution system. In the context of this application, a computer-usable or computer-readable medium may be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.

Claims

1. A method of associating attribute information, the method comprising:

determining the second attribute information associated with the first attribute information according to the first historical data set and the second historical data set associated with the first historical data set;

wherein, after determining the second attribute information associated with the first attribute information, the method further comprises:

2. The method of claim 1, wherein the determining a second historical data set of a second platform associated with the first historical data set that contains second attribute information comprises:

3. The method of claim 1, wherein determining the second attribute information associated with the first attribute information based on the first set of historical data and the second set of historical data associated with the first set of historical data comprises:

4. The method of claim 3, wherein a quantity of first attribute information included in the first set of historical data is the same as a quantity of second attribute information included in the associated second set of historical data;

the determining, according to the number of first attribute information included in the selected first historical data set and the number of second attribute information included in a second historical data set associated with the selected first historical data set, the second attribute information associated with the first attribute information includes:

5. The method of claim 3, wherein a quantity of first attribute information included in the first set of historical data is the same as a quantity of second attribute information included in the associated second set of historical data;

6. The method of claim 1, wherein the other objects on the first platform that are associated with the first target object are determined according to some or all of the following:

determining other objects on the first platform which are related to the second target object according to the picture in the first platform;

determining other objects which are related to the second target object on the first platform according to second attribute information which is related to the first attribute information of the second target object and second attribute information which is related to the first attribute information of the other objects;

and determining other objects which are related to the first target object on the first platform according to the identification corresponding to the first attribute information of the first target object and the identifications corresponding to the first attribute information of the other objects.

7. A system for associating attribute information, the system comprising:

an information association module, configured to determine, according to the first historical data set and the second historical data set associated with the first historical data set, the second attribute information associated with the first attribute information;

wherein the information association module is further configured to: determining other objects which are related to a first target object on a first platform aiming at first attribute information of a second target object in the first platform; determining first attribute information which is the same as the first attribute information of the second target object in the first attribute information of the other objects; associating second attribute information associated with the determined first attribute information of the other object with the first attribute information of the second target object.

8. The system of claim 7, wherein the set association module is specifically configured to:

9. The system of claim 7, wherein the information association module is specifically configured to:

10. The system of claim 9, wherein a quantity of first attribute information included in the first set of historical data is the same as a quantity of second attribute information included in the associated second set of historical data;

the information association module is specifically configured to:

11. The system of claim 9, wherein a quantity of first attribute information included in the first set of historical data is the same as a quantity of second attribute information included in the associated second set of historical data;

the information association module is specifically configured to:

12. The system of claim 7, wherein the information association module is specifically configured to determine other objects on the first platform that are associated with the first target object according to some or all of the following:

13. A method of generating a data set, the method comprising:

wherein the incidence relation is determined according to the following mode:

14. A system for generating a data set, the system comprising:

wherein the incidence relation is determined according to the following mode: