CN109903105B - Method and device for perfecting target commodity attributes - Google Patents
Method and device for perfecting target commodity attributes Download PDFInfo
- Publication number
- CN109903105B CN109903105B CN201711292885.7A CN201711292885A CN109903105B CN 109903105 B CN109903105 B CN 109903105B CN 201711292885 A CN201711292885 A CN 201711292885A CN 109903105 B CN109903105 B CN 109903105B
- Authority
- CN
- China
- Prior art keywords
- attribute
- basic
- original
- name
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Landscapes
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a method and a device for improving the attribute of a target commodity, and relates to the technical field of computers. One embodiment of the method comprises: according to the classification of the target commodity, acquiring a basic attribute name set and a basic attribute value set corresponding to the classification; comparing the basic attribute name corresponding to the original attribute name of the target commodity with each basic attribute name in the basic attribute name set to determine the attribute name to be modified; counting the occurrence times of each basic attribute value in the basic attribute value set in the commodity information, confirming that the original attribute value of the target commodity is different from the corresponding basic attribute value with the maximum occurrence times, and taking the basic attribute value with the maximum occurrence times as the attribute value to be modified; and modifying the attribute of the target commodity according to the attribute name to be modified and the attribute value to be modified. The implementation method avoids manual participation in the modification process of the commodity attribute, is high in efficiency and accuracy, and improves user experience.
Description
Technical Field
The invention relates to the field of computers, in particular to a method and a device for perfecting attributes of target commodities.
Background
When the e-commerce platform publishes the attribute information of the commodity, the attribute of the commodity is generally improved by manual editing and modification in a background system of the e-commerce platform by a worker according to the knowledge of the worker on the commodity and the attribute recommendation of the e-commerce platform on the commodity.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art: the number of commodities of the E-commerce platform is hundreds of millions, and due to the uncertainty of the attributes of the commodities and the manual editing and modifying mode, the issued attribute information is strong in subjectivity, has wrong attributes or missing attributes, is low in efficiency and is poor in user experience.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for improving an attribute of a target commodity, where an attribute name to be modified and an attribute value to be modified of the target commodity are determined based on an original attribute name and an original attribute value of the target commodity and a basic attribute name set and a basic attribute value set that are established according to commodity attribute data of each category, so as to modify an attribute of the commodity. By the mode, manual participation is avoided, efficiency is high, accuracy is high, and user experience is improved.
To achieve the above object, according to an aspect of the embodiments of the present invention, a method for perfecting attributes of a target commodity is provided.
The method for perfecting the attributes of the target commodity comprises the following steps: according to the classification of the target commodity, acquiring a basic attribute name set and a basic attribute value set corresponding to the classification; comparing the basic attribute name corresponding to the original attribute name of the target commodity with each basic attribute name in the basic attribute name set to determine an attribute name to be modified; counting the occurrence times of each basic attribute value in the basic attribute value set in the commodity information, confirming that the original attribute value of the target commodity is different from the corresponding basic attribute value with the largest occurrence time, and taking the basic attribute value with the largest occurrence time as an attribute value to be modified; and modifying the attribute of the target commodity according to the attribute name to be modified and the attribute value to be modified.
Optionally, before the step of classifying according to the target product, the method further includes: respectively establishing a basic attribute name set and a basic attribute value set for each classification according to the commodity attribute data of each classification; wherein the product attribute data includes the original attribute name and a corresponding original attribute value.
Optionally, the establishing a set of the basic attribute names and a set of the basic attribute values for each classification according to the commodity attribute data of each classification respectively includes: respectively merging the original attribute names with the same meaning and the original attribute values with the same meaning in each classification; taking the original attribute name with the largest occurrence frequency in the original attribute names combined in each classification as the basic attribute name of the classification, and taking the original attribute value with the largest occurrence frequency in the original attribute values combined in each classification as the basic attribute value of the classification; all basic attribute names of each class form the set of basic attribute names for that class, and all basic attribute values form the set of basic attribute values for that class.
Optionally, after the step of using the original attribute name with the largest occurrence number in the original attribute names combined in each category as the basic attribute name of the category, and before the step of using the original attribute value with the largest occurrence number in the original attribute values combined in each category as the basic attribute value of the category, the method further includes: constructing a bipartite graph for each classification.
Optionally, after the step of constructing the bipartite graph for each category and before the step of comparing the basic attribute name corresponding to the original attribute name of the target product with each basic attribute name in the basic attribute name set to determine the attribute name to be modified, the method further includes: establishing a mapping relation in an inverted index mode according to the bipartite graph; the mapping relationship comprises a mapping relationship established by taking the merged original attribute name as a key and the basic attribute name as a value, a mapping relationship established by taking the merged original attribute value as a key and the basic attribute value as a value, a mapping relationship established by taking the basic attribute value as a key and the merged original attribute value as a value, and a mapping relationship established by taking the basic attribute name as a key and the basic attribute value as a value.
Optionally, the comparing the basic attribute name corresponding to the original attribute name of the target product with each basic attribute name in the basic attribute name set to determine the attribute name to be modified includes: acquiring a basic attribute name corresponding to the original attribute name of the target commodity according to the original attribute name of the target commodity and the mapping relation between the combined original attribute name and the basic attribute name; and comparing the basic attribute names corresponding to the original attribute names of the target commodity with the basic attribute names in the basic attribute name set one by one, and taking the basic attribute names in different basic attribute name sets as attribute names to be modified.
Optionally, the counting the occurrence frequency of each basic attribute value in the basic attribute value set in the commodity information, determining that the original attribute value of the target commodity is different from the corresponding basic attribute value with the largest occurrence frequency, and taking the basic attribute value with the largest occurrence frequency as the attribute value to be modified includes: acquiring a merged original attribute value corresponding to the attribute name to be modified according to the attribute name to be modified and the mapping relation between the basic attribute name and the merged original attribute value; confirming that the combined original attribute value corresponding to the attribute name to be modified exists in the commodity information, and mapping the combined original attribute value to be the basic attribute value; and counting the occurrence times of the basic attribute values obtained by mapping in the commodity information, confirming that the original attribute value of the target commodity is different from the corresponding basic attribute value with the maximum occurrence times, and taking the basic attribute value with the maximum occurrence times as the attribute value to be modified.
To achieve the above object, according to an aspect of the embodiments of the present invention, there is provided an apparatus for perfecting attributes of a target commodity.
The device for perfecting the attributes of the target commodity comprises the following components: the acquisition module is used for acquiring a basic attribute name set and a basic attribute value set corresponding to the classification according to the classification of the target commodity; the attribute name to be modified determining module is used for comparing a basic attribute name corresponding to the original attribute name of the target commodity with each basic attribute name in the basic attribute name set so as to determine the attribute name to be modified; the attribute value to be modified determining module is used for counting the occurrence times of each basic attribute value in the basic attribute value set in the commodity information, confirming that the original attribute value of the target commodity is different from the corresponding basic attribute value with the largest occurrence time, and taking the basic attribute value with the largest occurrence time as the attribute value to be modified; and the modification module is used for modifying the attribute of the target commodity according to the attribute name to be modified and the attribute value to be modified.
Optionally, the apparatus further comprises: the establishing module is used for respectively establishing a basic attribute name set and a basic attribute value set for each classification according to the commodity attribute data of each classification; wherein the product attribute data includes the original attribute name and a corresponding original attribute value.
Optionally, the establishing module is further configured to: respectively merging the original attribute names with the same meaning and the original attribute values with the same meaning in each classification; taking the original attribute name with the largest occurrence frequency in the original attribute names combined in each classification as the basic attribute name of the classification, and taking the original attribute value with the largest occurrence frequency in the original attribute values combined in each classification as the basic attribute value of the classification; and all basic attribute names of each classification form the basic attribute name set of the classification, and all basic attribute values form the basic attribute value set of the classification.
Optionally, the apparatus further comprises: and the bipartite graph construction module is used for constructing the bipartite graph of each classification.
Optionally, the apparatus further comprises: the mapping relation establishing module is used for establishing a mapping relation in an inverted index mode according to the bipartite graph; the mapping relationship comprises a mapping relationship established by taking the merged original attribute name as a key and the basic attribute name as a value, a mapping relationship established by taking the merged original attribute value as a key and the basic attribute value as a value, a mapping relationship established by taking the basic attribute value as a key and the merged original attribute value as a value, and a mapping relationship established by taking the basic attribute name as a key and the basic attribute value as a value.
Optionally, the attribute name determining module to be modified is further configured to: acquiring a basic attribute name corresponding to the original attribute name of the target commodity according to the original attribute name of the target commodity and the mapping relation between the combined original attribute name and the basic attribute name; and comparing the basic attribute names corresponding to the original attribute names of the target commodity with the basic attribute names in the basic attribute name set one by one, and taking the basic attribute names in different basic attribute name sets as attribute names to be modified.
Optionally, the module for determining the attribute value to be modified is further configured to: acquiring a merged original attribute value corresponding to the attribute name to be modified according to the attribute name to be modified and the mapping relation between the basic attribute name and the merged original attribute value; confirming that the combined original attribute value corresponding to the attribute name to be modified exists in the commodity information, and mapping the combined original attribute value to be the basic attribute value; and counting the occurrence times of the basic attribute values obtained by mapping in the commodity information, confirming that the original attribute value of the target commodity is different from the corresponding basic attribute value with the maximum occurrence times, and taking the basic attribute value with the maximum occurrence times as the attribute value to be modified.
To achieve the above object, according to still another aspect of embodiments of the present invention, there is provided an electronic apparatus.
An electronic device of an embodiment of the present invention includes: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement a method for refining attributes of a target commodity according to an embodiment of the present invention.
To achieve the above object, according to still another aspect of embodiments of the present invention, there is provided a computer-readable medium.
A computer-readable medium of an embodiment of the present invention has a computer program stored thereon, and when the program is executed by a processor, the program implements a method of perfecting attributes of a target commodity of an embodiment of the present invention.
One embodiment of the above invention has the following advantages or benefits: determining the attribute name to be modified and the attribute value to be modified of the target commodity through the original attribute name and the original attribute value of the target commodity and the basic attribute name set and the basic attribute value set established according to the commodity attribute data of each classification, further modifying the commodity attribute, avoiding manual participation, and having high efficiency and high accuracy; the attribute of the target commodity is modified based on the bipartite graph, so that the attribute data of the commodity is more accurate and complete, and the user experience is improved.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of the main steps of a method of refining attributes of a target commodity according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart diagram illustrating a method for refining attributes of a target commodity in accordance with an embodiment of the present invention;
FIG. 3 is a schematic diagram of the major modules of an apparatus for refining attributes of a target commodity according to an embodiment of the present invention;
FIG. 4 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
FIG. 5 is a schematic diagram of a computer apparatus suitable for use in an electronic device to implement an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram of the main steps of a method for refining the attributes of a target commodity according to an embodiment of the present invention. As shown in fig. 1, the method for perfecting attributes of a target commodity according to an embodiment of the present invention mainly includes the following steps:
step S101: and acquiring a basic attribute name set and a basic attribute value set corresponding to the classification according to the classification of the target commodity. The target product is any product which needs attribute improvement, the attribute improvement may be completion (completion is for the case that the product attribute data is incomplete) or correction (correction is for the case that the product attribute data is incorrect), for example, and the product attribute data includes an original attribute name and a corresponding original data value. In addition, it is necessary to acquire as many kinds of commodity attribute data as possible in advance, and establish a basic attribute name set and a corresponding basic attribute value for each classification according to the commodity attribute data, and when the target commodity attribute needs to be improved, it is sufficient to directly acquire the basic attribute name set and the corresponding basic attribute value for the classification.
Step S102: and comparing the basic attribute name corresponding to the original attribute name of the target commodity with each basic attribute name in the basic attribute name set to determine the attribute name to be modified. Firstly, acquiring a basic attribute name corresponding to the original attribute name of the target commodity according to the original attribute name of the target commodity and the mapping relation between the combined original attribute name and the basic attribute name; and then comparing the basic attribute names corresponding to the original attribute names of the target commodity with the basic attribute names in the basic attribute name set one by one, and taking the basic attribute names in different basic attribute name sets as attribute names to be modified. The merged original attribute names are obtained by respectively merging the original attribute names with the same meaning in each classification in advance.
Step S103: and counting the occurrence times of each basic attribute value in the basic attribute value set in the commodity information, confirming that the original attribute value of the target commodity is different from the corresponding basic attribute value with the maximum occurrence times, and taking the basic attribute value with the maximum occurrence times as the attribute value to be modified. Firstly, acquiring a merged original attribute value corresponding to the attribute name to be modified according to the attribute name to be modified and the mapping relation between the basic attribute name and the merged original attribute value; then confirming that the combined original attribute value corresponding to the attribute name to be modified exists in the commodity information, and mapping the combined original attribute value to be the basic attribute value; and finally, counting the occurrence times of the basic attribute values obtained by mapping in the commodity information, confirming that the original attribute value of the target commodity is different from the corresponding basic attribute value with the maximum occurrence times, and taking the basic attribute value with the maximum occurrence times as the attribute value to be modified. The merged original attribute values are obtained by respectively merging the original attribute values with the same meaning in each classification in advance.
Step S104: and modifying the attribute of the target commodity according to the attribute name to be modified and the attribute value to be modified. And modifying the commodity attribute data of the target commodity according to the attribute name to be modified and the attribute value to be modified.
In a preferred embodiment of the present invention, the product attribute data is first obtained from the data mart, and since the specification attribute and the non-specification attribute exist in the product attribute, the two types need to be processed separately. The specification attribute and the non-specification attribute need to be judged in advance, for example, if the ratio of numbers to English in the original attribute value exceeds 80%, the specification attribute is judged, and if the ratio is less than 80%, the non-specification attribute is judged. For example, the original attribute name is weight, the original attribute value is 10g, and the ratio of number to English is 100%, then the attribute is specification attribute; the original attribute name is color, the original attribute value is green, the ratio of number to English is 0%, and the attribute is a non-specification attribute. The embodiment of the invention is suitable for the perfection of the commodity attributes with non-specification attributes.
Fig. 2 is a schematic main flow diagram of a method for refining the attributes of a target commodity according to an embodiment of the present invention. As shown in fig. 2, the method for perfecting attributes of a target commodity according to the embodiment of the present invention mainly includes the following steps:
step S201: and acquiring the commodity attribute data of each classification from the data mart. A data mart is a repository that collects data from operational data and other data sources that serve a particular group of professionals. The classification of the goods refers to a process of dividing the goods into several categories according to the attributes and characteristics of the goods. The commodity attribute data comprises original attribute names and corresponding original attribute values, wherein the original attribute names comprise colors and materials, and the original attribute values comprise green and cotton and linen. In the embodiment, commodity attribute data of each classification in the self-transaction platform and the friend-transaction platform, such as commodity attribute data of classifications of men's clothing, women's clothing, electrical appliances and the like, are acquired from the data mart through Spark. The Spark is a fast and general computing engine specially designed for large-scale data processing, and is suitable for MapReduce (mapping induction) algorithms which need iteration, such as data mining and machine learning. The more the obtained commodity attribute data is, the more reasonable the target commodity attribute is subsequently completed, so that in the embodiment, the commodity attribute data of the friend trading platform is obtained in addition to the commodity attribute data of the self trading platform.
Step S202: and respectively combining the original attribute names with the same meaning and the original attribute values with the same meaning in each classification. In the embodiment, the acquired original attribute names are merged in the same meaning in a manual marking mode, for example, in the classification of women's dresses, colors and commodity colors in the original attribute names are merged into colors, and materials, fabrics, commodity fabrics and commodity materials are merged into materials; the obtained original attribute values are combined in the same meaning in a manual labeling mode, the classification of women's dresses is taken as an example, Green series and Green in the original attribute values are combined into Green, and cotton flax fabrics are combined into cotton and flax.
Step S203: according to the merging result of each category in step S202, the original attribute name with the largest number of occurrences among the original attribute names merged in each category is used as the base attribute name of the category, and the original attribute value with the largest number of occurrences among the original attribute values merged in each category is used as the base attribute value of the category. For example, in the classification of women's dresses, the colors in the combined original attribute names appear 16 times, and the colors of the commodities appear 4 times, then the colors are used as a basic attribute name; and if the combined original attribute names have 12 times of materials, 10 times of materials, 6 times of commodity materials and 8 times of commodity materials, taking the materials as another basic attribute name. The basic attribute value is obtained in the same manner as the basic attribute name.
Step S204: all basic attribute names of each class form the set of basic attribute names for that class, and all basic attribute values form the set of basic attribute values for that class. For example, in the classification of women's clothing, the base attribute name may include color, material, and the like, the base attribute value corresponding to the color may include green, red, yellow, and the like, the base attribute name corresponding to the material may include cotton, hemp, fiber, silk, and the like, all the base attribute names of the classification form a base attribute name set of the classification, all the base attribute values form a base attribute value set of the classification, and the base attribute name set and the base attribute value set form a base attribute set.
Step S205: and constructing a bipartite graph of each classification according to the corresponding relation between the basic attribute name and the combined original attribute name, the corresponding relation between the basic attribute value and the combined original attribute value, and the corresponding relation between the basic attribute name and the basic attribute value in each classification. The bipartite graph is a special model in graph theory, and if G ═ V, E is an undirected graph, if a vertex V can be divided into two mutually disjoint subsets (a, B), and two vertices i and j associated with each edge (i, j) in the graph belong to the two different vertex sets (i in a, j in B), respectively, then the graph G is called a bipartite graph.
Step S206: establishing four mapping relations according to the bipartite graph; the mapping relationship comprises a mapping relationship established by taking the merged original attribute name as a Key (Key) and the basic attribute name as a Value (Value), a mapping relationship established by taking the merged original attribute Value as the Key and the basic attribute Value as the Value, a mapping relationship established by taking the basic attribute Value as the Key and the merged original attribute Value as the Value, and a mapping relationship established by taking the basic attribute name as the Key and the basic attribute Value as the Value. In the embodiment, the mapping relationship may be established in an inverted index manner, or in a multi-table association manner.
Step S207: and acquiring a basic attribute set corresponding to the classification according to the classification of the target commodity. The target product is any product obtained from the database, and the attribute perfection may be, for example, completion (completion is for the case that the product attribute data is incomplete) or correction (correction is for the case that the product attribute data is incorrect). For example, if the target commodity is a suit-dress, the basic attribute set corresponding to the classification of the suit-dress is obtained. The basic attribute names in the basic attribute set are assumed to be color, material, style and style, and the corresponding basic attribute values are green, red and yellow, cotton, fiber, silk, long, medium and long, short, academy wind, street wind and commuter wind.
Step S208: and acquiring commodity data of the target commodity, circularly traversing the original attribute names in the commodity attribute data, and acquiring the basic attribute name corresponding to each original attribute name of the target commodity according to the combined mapping relation between the original attribute names and the basic attribute names. The commodity data comprises SKU (product uniform serial number, each product corresponds to a unique SKU), commodity attribute data and commodity information, and the commodity information comprises a commodity title, specification parameters and commodity description. For example, if the original attribute name of the target commodity is the material, style and style of the commodity, the corresponding basic attribute name is the material, style and style.
Step S209: comparing the basic attribute name corresponding to each original attribute name of the target commodity with the basic attribute name set of the target commodity in corresponding classification one by one, and if the basic attribute names are not identical, executing step S210; if both are identical, step S211 is performed. The following description will be made of a case where the two are not completely identical:
the first embodiment is as follows: if all basic attribute names corresponding to each original attribute name of the target commodity are materials, styles and styles, the number of the basic attribute names is 3; and if all the basic attribute names in the basic attribute name set of the corresponding classification of the target commodity are color, material, style and style, and the number of the basic attribute names is 4, the commodity attribute data of the target commodity is incomplete, and the two basic attribute names are compared one by one to find out the missing basic attribute name color.
Example two: if all the basic attribute names corresponding to each original attribute name of the target commodity are weight, material, style and style, and all the basic attribute names in the representative name attribute set corresponding to the classification of the target commodity are color, material, style and style, it can be obtained that the color in the basic attribute name set is different from the weight corresponding to the original attribute name of the target commodity.
Step S210: and taking the basic attribute names in different basic attribute name sets as attribute names to be modified, and acquiring the combined original attribute values corresponding to the attribute names to be modified according to the attribute names to be modified and the mapping relation between the basic attribute names and the combined original attribute values. The attribute name to be modified is the attribute name which needs to be perfected for the commodity. The following description will be made on two cases, namely, the case requiring completion and the case requiring correction (corresponding to the first embodiment): in the first embodiment, the missing basic attribute name "color" is the attribute name to be modified that needs to be filled. In the second embodiment, the basic attribute name of "weight" is the name of the attribute to be modified that needs to be modified.
In the embodiment, when a merged original attribute value corresponding to the attribute name to be modified is obtained, a basic attribute value corresponding to the attribute name to be modified is obtained through the mapping relation by taking the attribute name to be modified as a Key according to the attribute name to be modified and the mapping relation between the basic attribute name and the basic attribute value; and then according to the mapping relation between the basic attribute value and the merged original attribute value, taking the basic attribute value as Key to obtain the merged original attribute value corresponding to the attribute name to be modified through the mapping relation. For example, if the attribute name to be modified of the target commodity is color, the corresponding combined original attribute values are Green, Red, Yellow.
Step S211: and acquiring the combined original attribute value corresponding to each original attribute name according to the basic attribute name corresponding to each original attribute name and the mapping relation between the basic attribute name and the combined original attribute value. In a preferred embodiment, the step only needs to obtain the merged original attribute value corresponding to the original attribute name without the original attribute name needing to be modified.
Step S212: according to the merged original attribute values obtained in steps S210 and S211, the product information such as the product title, the product description, and the specification parameter is searched for the merged original attribute value, the corresponding occurrence times in the product title, the product description, and the specification parameter are recorded, and the corresponding basic attribute value is obtained according to the mapping relationship between the merged original attribute value and the basic attribute value by using the merged original attribute value as a Key. For example, if Green appears 1 time in the title of the product, a Green system appears 5 times, red appears 2 times in the specification parameters, and Green appears 1 time in the description of the product, then the basic attribute values corresponding to Green, the Green system, red and Green, that is, Green and red, are found out through the mapping relationship.
Step S213: counting the occurrence frequency of the basic attribute value mapped in step S212 in the commodity information, confirming that the original attribute value of the target commodity is different from the corresponding basic attribute value with the largest occurrence frequency, and taking the basic attribute value with the largest occurrence frequency as the attribute value to be modified. For example, the original attribute name of the target commodity is color, the original attribute value is red, the mapped basic attribute value corresponding to the original attribute name is green and red, if the occurrence frequency of the basic attribute value of green in the commodity information is more than that of red, the original attribute value needs to be modified into green, and therefore the green is used as the attribute value to be modified; if the occurrence times of green and red are the same, taking out the basic attribute value with the most current times as the attribute value to be modified according to the sequence of the rule parameter > commodity title > commodity description (the sequence is obtained by manual experience); if the number of occurrences of red is greater than the number of occurrences of green, the original attribute value need not be modified.
Step S214: and modifying the commodity attribute of the target commodity according to the determined attribute name to be modified and the attribute value to be modified. Filling or correcting the commodity attribute data of the target commodity according to the attribute name to be modified and the attribute value to be modified.
Step S215: and unifying all the commodity attribute data of the target commodity into a basic attribute name and a basic attribute value according to the mapping relation. The commodity attribute data in the step comprises an original attribute name, an original attribute value, an attribute name to be modified and an attribute value to be modified.
According to the method for improving the target commodity attribute, the attribute name to be modified and the attribute value to be modified of the target commodity are determined through the original attribute name and the original attribute value of the target commodity and the basic attribute name set and the basic attribute value set which are established according to the commodity attribute data of each classification, so that the commodity attribute is modified, manual participation is avoided, the efficiency is high, and the accuracy is high; the attribute of the target commodity is modified based on the bipartite graph, so that the attribute data of the commodity is more accurate and complete, and the user experience is improved.
Fig. 3 is a schematic diagram of the main modules of an apparatus for refining the attributes of a target commodity according to an embodiment of the present invention. As shown in fig. 3, the apparatus 300 for perfecting attributes of target products according to the embodiment of the present invention mainly includes:
the obtaining module 301 is configured to obtain, according to the classification of the target product, a basic attribute name set and a basic attribute value set corresponding to the classification. The target product is any product which needs attribute improvement, the attribute improvement may be completion (completion is for the case that the product attribute data is incomplete) or correction (correction is for the case that the product attribute data is incorrect), for example, and the product attribute data includes an original attribute name and a corresponding original data value. In addition, the basic attribute name set and the basic attribute value set of each classification are generated based on the commodity attribute data of as many types as possible acquired in advance.
The attribute name to be modified determining module 302 is configured to compare a basic attribute name corresponding to the original attribute name of the target product with each basic attribute name in the basic attribute name set, so as to determine an attribute name to be modified. The module needs to obtain a basic attribute name corresponding to the original attribute name of the target commodity according to the original attribute name of the target commodity and the mapping relation between the combined original attribute name and the basic attribute name; and comparing the basic attribute names corresponding to the original attribute names of the target commodity with the basic attribute names in the basic attribute name set one by one, and taking the basic attribute names in different basic attribute name sets as attribute names to be modified. The merged original attribute names are obtained by respectively merging the original attribute names with the same meaning in each classification in advance.
And a to-be-modified attribute value determining module 303, configured to count the occurrence frequency of each basic attribute value in the basic attribute value set in the commodity information, determine that the original attribute value of the target commodity is different from the corresponding basic attribute value with the largest occurrence frequency, and use the basic attribute value with the largest occurrence frequency as the to-be-modified attribute value. The module needs to obtain a merged original attribute value corresponding to the attribute name to be modified according to the attribute name to be modified and the mapping relation between the basic attribute name and the merged original attribute value; confirming that the combined original attribute value corresponding to the attribute name to be modified exists in the commodity information, and mapping the combined original attribute value to be the basic attribute value; and counting the occurrence times of the basic attribute values obtained by mapping in the commodity information, confirming that the original attribute value of the target commodity is different from the corresponding basic attribute value with the maximum occurrence times, and taking the basic attribute value with the maximum occurrence times as the attribute value to be modified. The merged original attribute values are obtained by respectively merging the original attribute values with the same meaning in each classification in advance.
And the modifying module 304 is configured to modify the attribute of the target product according to the attribute name to be modified and the attribute value to be modified. And modifying the commodity attribute data of the target commodity according to the attribute name to be modified and the attribute value to be modified.
In addition, the apparatus 300 for perfecting the attributes of the target product according to the embodiment of the present invention may further include: the system comprises an establishing module, a bipartite graph establishing module and a mapping relation establishing module (not shown in the figure). The establishing module is used for respectively establishing a basic attribute name set and a basic attribute value set for each classification according to the commodity attribute data of each classification; the bipartite graph construction module is used for constructing a bipartite graph of each classification; and the mapping relation establishing module is used for establishing the mapping relation in an inverted index mode according to the bipartite graph.
From the above description, it can be seen that the attribute name to be modified and the attribute value to be modified of the target commodity are determined through the original attribute name and the original attribute value of the target commodity and the basic attribute name set and the basic attribute value set established according to the commodity attribute data of each classification, so that the commodity attribute is modified, manual participation is avoided, the efficiency is high, and the accuracy is high; the attribute of the target commodity is modified based on the bipartite graph, so that the attribute data of the commodity is more accurate and complete, and the user experience is improved.
Fig. 4 illustrates an exemplary system architecture 400 of a method of processing real-time messages or a system for processing real-time messages to which embodiments of the present invention may be applied.
As shown in fig. 4, the system architecture 400 may include terminal devices 401, 402, 403, a network 404, and a server 405. The network 404 serves as a medium for providing communication links between the terminal devices 401, 402, 403 and the server 405. Network 404 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few.
A user may use terminal devices 401, 402, 403 to interact with a server 405 over a network 404 to receive or send messages or the like. The terminal devices 401, 402, 403 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 401, 402, 403 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 405 may be a server providing various services, such as a background management server (for example only) providing support for click events generated by users using the terminal devices 401, 402, 403. The background management server may analyze and perform other processing on the received click data, text content, and other data, and feed back a processing result (for example, target push information, product information — just an example) to the terminal device.
It should be noted that the method for refining the target product attribute provided in the embodiment of the present application is generally executed by the server 405, and accordingly, the apparatus for refining the target product attribute is generally disposed in the server 405.
It should be understood that the number of terminal devices, networks, and servers in fig. 4 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
The invention also provides an electronic device and a computer readable medium according to the embodiment of the invention.
The electronic device of the present invention includes: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement a method for refining attributes of a target commodity according to an embodiment of the present invention.
The computer readable medium of the present invention has stored thereon a computer program which, when executed by a processor, implements a method of refining attributes of a target commodity according to an embodiment of the present invention.
Referring now to FIG. 5, shown is a block diagram of a computer system 500 suitable for use in implementing an electronic device of an embodiment of the present invention. The electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 5, the computer system 500 includes a Central Processing Unit (CPU)501 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the computer system 500 are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output portion 507 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The driver 510 is also connected to the I/O interface 505 as necessary. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as necessary, so that a computer program read out therefrom is mounted into the storage section 508 as necessary.
In particular, the processes described above with respect to the main step diagrams may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program containing program code for performing the method illustrated in the main step diagram. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 509, and/or installed from the removable medium 511. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 501.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor comprises an acquisition module, an attribute name to be modified determining module, an attribute value to be modified determining module and a modification module. The names of the modules do not form a limitation on the modules themselves under certain conditions, for example, the obtaining module may also be described as a "module for obtaining a basic attribute name set and a basic attribute value set corresponding to a classification according to the classification of a target commodity".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: according to the classification of the target commodity, acquiring a basic attribute name set and a basic attribute value set corresponding to the classification; comparing the basic attribute name corresponding to the original attribute name of the target commodity with each basic attribute name in the basic attribute name set to determine an attribute name to be modified; counting the occurrence times of each basic attribute value in the basic attribute value set in the commodity information, confirming that the original attribute value of the target commodity is different from the corresponding basic attribute value with the largest occurrence time, and taking the basic attribute value with the largest occurrence time as an attribute value to be modified; and modifying the attribute of the target commodity according to the attribute name to be modified and the attribute value to be modified.
From the above description, it can be seen that the attribute name to be modified and the attribute value to be modified of the target commodity are determined through the original attribute name and the original attribute value of the target commodity and the basic attribute name set and the basic attribute value set established according to the commodity attribute data of each classification, so that the commodity attribute is modified, manual participation is avoided, the efficiency is high, and the accuracy is high; the attribute of the target commodity is modified based on the bipartite graph, so that the attribute data of the commodity is more accurate and complete, and the user experience is improved.
The product can execute the method provided by the embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the method provided by the embodiment of the present invention.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (11)
1. A method for refining attributes of a target commodity, comprising:
according to the classification of the target commodity, acquiring a basic attribute name set and a basic attribute value set corresponding to the classification;
comparing the basic attribute name corresponding to the original attribute name of the target commodity with each basic attribute name in the basic attribute name set to determine an attribute name to be modified;
counting the occurrence times of each basic attribute value in the basic attribute value set in the commodity information, confirming that the original attribute value of the target commodity is different from the corresponding basic attribute value with the largest occurrence time, and taking the basic attribute value with the largest occurrence time as an attribute value to be modified;
modifying the attribute of the target commodity according to the attribute name to be modified and the attribute value to be modified;
before acquiring a basic attribute name set and a basic attribute value set corresponding to a classification according to the classification of a target commodity, the method comprises the following steps:
constructing a bipartite graph of each classification according to the corresponding relation between the basic attribute name and the combined original attribute name, the corresponding relation between the basic attribute value and the combined original attribute value, and the corresponding relation between the basic attribute name and the basic attribute value in each classification, and establishing a mapping relation according to the bipartite graph in an inverted index mode; respectively merging the original attribute names with the same meaning and the original attribute values with the same meaning in each classification to obtain merged original attribute names and merged original attribute values;
wherein, before the step of classifying according to the target commodity, the method further comprises: respectively establishing a basic attribute name set and a basic attribute value set for each classification according to the commodity attribute data of each classification; wherein the commodity attribute data includes the original attribute name and a corresponding original attribute value, including:
respectively merging the original attribute names with the same meaning and the original attribute values with the same meaning in each classification;
taking the original attribute name with the largest occurrence frequency in the original attribute names combined in each classification as the basic attribute name of the classification, and taking the original attribute value with the largest occurrence frequency in the original attribute values combined in each classification as the basic attribute value of the classification;
all basic attribute names of each class form the set of basic attribute names for that class, and all basic attribute values form the set of basic attribute values for that class.
2. The method according to claim 1, wherein the mapping relationship comprises a mapping relationship established by using the merged original attribute name as a key and the basic attribute name as a value, a mapping relationship established by using the merged original attribute value as a key and the basic attribute value as a value, a mapping relationship established by using the basic attribute value as a key and the merged original attribute value as a value, and a mapping relationship established by using the basic attribute name as a key and the basic attribute value as a value.
3. The method according to claim 1 or 2, wherein the comparing the basic attribute name corresponding to the original attribute name of the target product with each basic attribute name in the basic attribute name set to determine the attribute name to be modified comprises:
acquiring a basic attribute name corresponding to the original attribute name of the target commodity according to the original attribute name of the target commodity and the mapping relation between the combined original attribute name and the basic attribute name;
and comparing the basic attribute names corresponding to the original attribute names of the target commodity with the basic attribute names in the basic attribute name set one by one, and taking the basic attribute names in different basic attribute name sets as attribute names to be modified.
4. The method according to claim 1 or 2, wherein the counting the occurrence number of each basic attribute value in the basic attribute value set in the commodity information, confirming that the original attribute value of the target commodity is different from the corresponding basic attribute value with the largest occurrence number, and using the basic attribute value with the largest occurrence number as the attribute value to be modified includes:
acquiring a merged original attribute value corresponding to the attribute name to be modified according to the attribute name to be modified and the mapping relation between the basic attribute name and the merged original attribute value;
confirming that the combined original attribute value corresponding to the attribute name to be modified exists in the commodity information, and mapping the combined original attribute value to be the basic attribute value;
and counting the occurrence times of the basic attribute values obtained by mapping in the commodity information, confirming that the original attribute value of the target commodity is different from the corresponding basic attribute value with the maximum occurrence times, and taking the basic attribute value with the maximum occurrence times as the attribute value to be modified.
5. An apparatus for refining attributes of a target commodity, comprising:
the acquisition module is used for acquiring a basic attribute name set and a basic attribute value set corresponding to the classification according to the classification of the target commodity; before acquiring a basic attribute name set and a basic attribute value set corresponding to a classification according to the classification of a target commodity, the method comprises the following steps: constructing a bipartite graph of each classification according to the corresponding relation between the basic attribute name and the combined original attribute name, the corresponding relation between the basic attribute value and the combined original attribute value, and the corresponding relation between the basic attribute name and the basic attribute value in each classification; respectively merging the original attribute names with the same meaning and the original attribute values with the same meaning in each classification to obtain merged original attribute names and merged original attribute values;
the mapping relation establishing module is used for establishing a mapping relation in an inverted index mode according to the bipartite graph;
the attribute name to be modified determining module is used for comparing a basic attribute name corresponding to the original attribute name of the target commodity with each basic attribute name in the basic attribute name set so as to determine the attribute name to be modified;
the attribute value to be modified determining module is used for counting the occurrence times of each basic attribute value in the basic attribute value set in the commodity information, confirming that the original attribute value of the target commodity is different from the corresponding basic attribute value with the largest occurrence time, and taking the basic attribute value with the largest occurrence time as the attribute value to be modified;
the modification module is used for modifying the attribute of the target commodity according to the attribute name to be modified and the attribute value to be modified;
the establishing module is used for respectively establishing a basic attribute name set and a basic attribute value set for each classification according to the commodity attribute data of each classification; wherein the commodity attribute data includes the original attribute name and a corresponding original attribute value, including: respectively merging the original attribute names with the same meaning and the original attribute values with the same meaning in each classification; taking the original attribute name with the largest occurrence frequency in the original attribute names combined in each classification as the basic attribute name of the classification, and taking the original attribute value with the largest occurrence frequency in the original attribute values combined in each classification as the basic attribute value of the classification; and all basic attribute names of each classification form the basic attribute name set of the classification, and all basic attribute values form the basic attribute value set of the classification.
6. The apparatus of claim 5, further comprising: and the bipartite graph construction module is used for constructing the bipartite graph of each classification.
7. The apparatus according to claim 6, wherein the mapping relationship comprises a mapping relationship established by using the merged original attribute name as a key and the basic attribute name as a value, a mapping relationship established by using the merged original attribute value as a key and the basic attribute value as a value, a mapping relationship established by using the basic attribute value as a key and the merged original attribute value as a value, and a mapping relationship established by using the basic attribute name as a key and the basic attribute value as a value.
8. The apparatus according to claim 5 or 7, wherein the attribute name to be modified determining module is further configured to:
acquiring a basic attribute name corresponding to the original attribute name of the target commodity according to the original attribute name of the target commodity and the mapping relation between the combined original attribute name and the basic attribute name; and
and comparing the basic attribute names corresponding to the original attribute names of the target commodity with the basic attribute names in the basic attribute name set one by one, and taking the basic attribute names in different basic attribute name sets as attribute names to be modified.
9. The apparatus according to claim 5 or 7, wherein the module for determining the attribute value to be modified is further configured to:
acquiring a merged original attribute value corresponding to the attribute name to be modified according to the attribute name to be modified and the mapping relation between the basic attribute name and the merged original attribute value;
confirming that the combined original attribute value corresponding to the attribute name to be modified exists in the commodity information, and mapping the combined original attribute value to be the basic attribute value; and
and counting the occurrence times of the basic attribute values obtained by mapping in the commodity information, confirming that the original attribute value of the target commodity is different from the corresponding basic attribute value with the maximum occurrence times, and taking the basic attribute value with the maximum occurrence times as the attribute value to be modified.
10. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-4.
11. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711292885.7A CN109903105B (en) | 2017-12-08 | 2017-12-08 | Method and device for perfecting target commodity attributes |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711292885.7A CN109903105B (en) | 2017-12-08 | 2017-12-08 | Method and device for perfecting target commodity attributes |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109903105A CN109903105A (en) | 2019-06-18 |
| CN109903105B true CN109903105B (en) | 2021-11-30 |
Family
ID=66940243
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711292885.7A Active CN109903105B (en) | 2017-12-08 | 2017-12-08 | Method and device for perfecting target commodity attributes |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109903105B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112699672B (en) * | 2019-10-23 | 2024-04-05 | 北京沃东天骏信息技术有限公司 | Method and device for selecting articles |
| CN110971974B (en) * | 2019-12-06 | 2022-02-15 | 北京小米移动软件有限公司 | Configuration parameter creating method, device, terminal and storage medium |
| CN113362093A (en) * | 2020-03-05 | 2021-09-07 | 北京沃东天骏信息技术有限公司 | Method, device and equipment for determining product target attribute and readable storage medium |
| CN113609112A (en) * | 2021-08-02 | 2021-11-05 | 北京值得买科技股份有限公司 | E-commerce commodity attribute data standardization processing method and system |
| CN115063211B (en) * | 2022-08-16 | 2022-11-11 | 华能能源交通产业控股有限公司 | Method and device for acquiring commodity attribute data |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102332009A (en) * | 2011-09-02 | 2012-01-25 | 北京大学 | Relational query method implemented on large-scale data set |
| CN103116639A (en) * | 2013-02-20 | 2013-05-22 | 新浪网技术(中国)有限公司 | Item recommendation method and system based on user-item bipartite model |
| CN103578015A (en) * | 2012-08-07 | 2014-02-12 | 阿里巴巴集团控股有限公司 | Method and device for achieving commodity attribute navigation |
| CN106033456A (en) * | 2015-03-18 | 2016-10-19 | 阿里巴巴集团控股有限公司 | Method and device for correcting attribute values of background attributes of goods |
| CN106776834A (en) * | 2016-11-28 | 2017-05-31 | 中通服公众信息产业股份有限公司 | A kind of data analysis based on index is from access method and system |
-
2017
- 2017-12-08 CN CN201711292885.7A patent/CN109903105B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102332009A (en) * | 2011-09-02 | 2012-01-25 | 北京大学 | Relational query method implemented on large-scale data set |
| CN103578015A (en) * | 2012-08-07 | 2014-02-12 | 阿里巴巴集团控股有限公司 | Method and device for achieving commodity attribute navigation |
| CN103116639A (en) * | 2013-02-20 | 2013-05-22 | 新浪网技术(中国)有限公司 | Item recommendation method and system based on user-item bipartite model |
| CN106033456A (en) * | 2015-03-18 | 2016-10-19 | 阿里巴巴集团控股有限公司 | Method and device for correcting attribute values of background attributes of goods |
| CN106776834A (en) * | 2016-11-28 | 2017-05-31 | 中通服公众信息产业股份有限公司 | A kind of data analysis based on index is from access method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109903105A (en) | 2019-06-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109903105B (en) | Method and device for perfecting target commodity attributes | |
| CN110689268B (en) | Method and device for extracting indexes | |
| CN110309142B (en) | Method and device for rule management | |
| CN113434527B (en) | Data processing method, device, electronic equipment and storage medium | |
| CN109976999B (en) | Method and device for measuring coverage rate of test cases | |
| CN111782850B (en) | Object searching method and device based on hand drawing | |
| CN112818026A (en) | Data integration method and device | |
| CN112947919A (en) | Method and device for constructing service model and processing service request | |
| CN110704481B (en) | Method and device for displaying data | |
| CN113190558A (en) | Data processing method and system | |
| CN111143408A (en) | Event processing method and device based on business rules | |
| CN111026629A (en) | Method and device for automatically generating test script | |
| CN114722048B (en) | Data processing method and device, electronic equipment and storage medium | |
| CN114817297A (en) | Method and device for processing data | |
| CN115454971A (en) | Data migration method and device, electronic equipment and storage medium | |
| CN113742321B (en) | Data updating method and device | |
| CN113760240B (en) | Method and device for generating data model | |
| CN112988857B (en) | Service data processing method and device | |
| CN113760695A (en) | Method and device for positioning problem code | |
| CN113554041B (en) | Method and device for marking labels for users | |
| CN115495518B (en) | Method and device for generating chart | |
| CN110874386A (en) | Method and device for establishing category mapping relation | |
| CN110874771A (en) | Method and device for matching commodities | |
| CN112905427B (en) | Data processing method and device | |
| CN112783956B (en) | Information processing method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |