CN110334306A - Label processing method and device - Google Patents
Label processing method and device Download PDFInfo
- Publication number
- CN110334306A CN110334306A CN201910544613.4A CN201910544613A CN110334306A CN 110334306 A CN110334306 A CN 110334306A CN 201910544613 A CN201910544613 A CN 201910544613A CN 110334306 A CN110334306 A CN 110334306A
- Authority
- CN
- China
- Prior art keywords
- shop
- label
- commodity
- under
- default
- 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.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/958—Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0621—Item configuration or customization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0641—Shopping interfaces
Abstract
The present invention relates to label processing method and the technical solutions of device.This method comprises: obtaining the first eigenvector of at least one default shop label；Obtain the second feature vector in the shop of label to be configured；According to the first eigenvector and the second feature vector, the current shop label in the shop of the label to be configured is determined.According to the technical solution of the present invention shop label can be configured for the shop of not label allocation automatically, improve the allocative efficiency of shop label, reduce manual operation burden, and ensure that the shop under same label significantly has attribute described in label, and the shop under label is the very high shop set of the degree of correlation；Simultaneously when the continuous restocking of commodity in shop, undercarriage, shop label can also be changed automatically, improve the correlation in shop and label.
Description
Technical field
The present invention relates to electric business technical field more particularly to label processing method and devices.
Background technique
Currently, using miscellaneous label, guidance user more browses related shop and quotient in purchase system
Product are a kind of very universal ways, and for example, there are a labels to be " local and special products ", user in micro- shop browsing local and special products commodity or
It is seen that the label when person local and special products shop, by clicking to enter label it is seen that more shops and commodity.
But these labels are usually all that electric business operator adds manually, and artificial is manually the shop on electric business platform
Add that label is time-consuming and laborious, low efficiency, while the commodity in shop are also in continuous restocking and undercarriage so that shop and label
Correlation gradually decreases, this just needs operator constantly to pay close attention to and changes shop label, further increases manual operation burden.
Summary of the invention
The embodiment of the invention provides label processing method and devices.The technical solution is as follows:
According to a first aspect of the embodiments of the present invention, a kind of label processing method is provided, comprising:
Obtain the first eigenvector of at least one default shop label；
Obtain the second feature vector in the shop of label to be configured；
According to the first eigenvector and the second feature vector, the current of the shop of the label to be configured is determined
Shop label.
In one embodiment, the method also includes:
After determining the current shop label in the shop of the label to be configured, the shop of the label to be configured is calculated
Under commodity third feature vector and the current shop label first distance；
According to the first distance, corresponding Commercial goods labels are configured for the commodity under the shop of the label to be configured.
In one embodiment, before the first eigenvector for obtaining at least one default shop label, the method
Further include:
Obtain the shop feature vector in each shop under each default shop label；
According to the shop feature vector in each shop, the first eigenvector of each default shop label is calculated.
In one embodiment, the shop feature vector for obtaining each shop under each default shop label, comprising:
Obtain the product features vector of all commodity under each default shop label under each shop；
According to the product features vector of all commodity, the shop for calculating each shop under each default shop label is special
Levy vector.
In one embodiment, the product features vector according to all commodity calculates each default shop
The shop feature vector in each shop under label, comprising:
According to the product features vector of all commodity under each shop, all commodity are clustered, N is obtained
A commercial articles clustering；
Determine the most class of commodity amount in N number of commercial articles clustering；
According to the product features vector of the commodity under the most class of the commodity amount, the shop for calculating each shop is special
Levy vector.
According to a second aspect of the embodiments of the present invention, a kind of label processing device is provided, comprising:
First obtains module, for obtaining the first eigenvector of at least one default shop label；
Second obtains module, the second feature vector in the shop for obtaining label to be configured；
Determining module, for determining the mark to be configured according to the first eigenvector and the second feature vector
The current shop label in the shop of label.
In one embodiment, described device further include:
First computing module, for calculating institute after determining the current shop label in the shop of the label to be configured
State the third feature vector of the commodity under the shop of label to be configured and the first distance of the current shop label；
Configuration module, for being configured for the commodity under the shop of the label to be configured corresponding according to the first distance
Commercial goods labels.
In one embodiment, described device further include:
Third obtains module, for obtaining each before the first eigenvector for obtaining at least one default shop label
The shop feature vector in each shop under default shop label；
Second computing module calculates each default shop label for the shop feature vector according to each shop
First eigenvector.
In one embodiment, the third acquisition module includes:
Acquisition submodule, for obtain the product features of all commodity under each default shop label under each shop to
Amount；
Computational submodule calculates each default shop label for the product features vector according to all commodity
Under each shop shop feature vector.
In one embodiment, the computational submodule includes:
Cluster cell, for the product features vector according to all commodity under each shop, by all commodity
It is clustered, obtains N number of commercial articles clustering；
Determination unit, for determining the most class of the commodity amount in N number of commercial articles clustering；
Computing unit, for the product features vector according to the commodity under the most class of the commodity amount, described in calculating
The shop feature vector in each shop.
The technical solution that the embodiment of the present invention provides can include the following benefits:
It, can be according to the of the shop of label to be configured by obtaining first eigenvector of at least one default shop label
Two feature vectors and above-mentioned first eigenvector judge second feature vector and which are pre- at least one default shop label
If the first eigenvector of shop label matches, and then the default shop label of matched first eigenvector is automatically determined
For the current shop label in the shop of the label to be configured, marked so as to configure shop automatically for the shop of not label allocation
Label improve the allocative efficiency of shop label, reduce manual operation burden, and ensure that the shop under same label significantly has mark
Described attribute is signed, and the shop under label is the very high shop set of the degree of correlation；Simultaneously on the commodity in shop are continuous
When frame, undercarriage, shop label can also be changed automatically, improve the correlation in shop and label.
It should be understood that above general description and following detailed description be only it is exemplary and explanatory, not
It can the limitation present invention.
Detailed description of the invention
The drawings herein are incorporated into the specification and forms part of this specification, and shows and meets implementation of the invention
Example, and be used to explain the principle of the present invention together with specification.
Fig. 1 is a kind of flow chart of label processing method shown according to an exemplary embodiment.
Fig. 2 is the flow chart of another label processing method shown according to an exemplary embodiment.
Fig. 3 is the flow chart of another label processing method shown according to an exemplary embodiment.
Fig. 4 is a kind of block diagram of label processing device shown according to an exemplary embodiment.
Fig. 5 is the block diagram of another label processing device shown according to an exemplary embodiment.
Specific embodiment
Example embodiments are described in detail here, and the example is illustrated in the accompanying drawings.Following description is related to
When attached drawing, unless otherwise indicated, the same numbers in different drawings indicate the same or similar elements.Following exemplary embodiment
Described in embodiment do not represent all embodiments consistented with the present invention.On the contrary, they be only with it is such as appended
The example of device and method being described in detail in claims, some aspects of the invention are consistent.
In order to solve the above-mentioned technical problem, the embodiment of the invention provides a kind of label processing method, this method can be used for
In tag processes program, system or device, and the corresponding executing subject of this method can be the terminals such as mobile phone or server, such as scheme
Shown in 1, the method comprising the steps of S101 to step S103:
In step s101, the first eigenvector of at least one default shop label is obtained；
At least one default shop label can be the shop label set in advance.
In step s 102, the second feature vector in the shop of label to be configured is obtained；
In step s 103, according to first eigenvector and second feature vector, working as the shop of label to be configured is determined
Preceding shop label.
It, can be according to the of the shop of label to be configured by obtaining first eigenvector of at least one default shop label
Two feature vectors and above-mentioned first eigenvector judge second feature vector and which are pre- at least one default shop label
If the first eigenvector of shop label matches, and then the default shop label of matched first eigenvector is automatically determined
For the current shop label in the shop of the label to be configured, marked so as to configure shop automatically for the shop of not label allocation
Label improve the allocative efficiency of shop label, reduce manual operation burden, and ensure that the shop under same label significantly has mark
Described attribute is signed, and the shop under label is the very high shop set of the degree of correlation；Simultaneously on the commodity in shop are continuous
When frame, undercarriage, shop label can also be changed automatically, improve the correlation in shop and label.
Secondly, in judging second feature vector and at least one default shop label which default shop label the
When one feature vector matches, the distance between second feature vector and each first eigenvector can be calculated, and then according to distance
Distance is determined to be matched with the first eigenvector of which default shop label.It certainly, can will be nearest with second feature vector distance
The default shop label of first eigenvector be determined as current shop label.
As shown in Fig. 2, in one embodiment, the above method further includes step S201 and step S202:
In step s 201, after determining the current shop label in the shop of label to be configured, label to be configured is calculated
Shop under commodity third feature vector and current shop label first distance；
In step S202, according to first distance, corresponding commodity mark is configured for the commodity under the shop of label to be configured
Label.
After the current shop label of determination, can calculate automatically the third feature of the commodity under the shop of label to be configured to
The first distance of amount and current shop label, and then be the mark to be configured according to the size of first distance and current shop label
Commodity under the shop of label automatically configure corresponding Commercial goods labels, thus it is automatically tagged for commodity, improve Commercial goods labels
Allocative efficiency avoids user's manual configuration Commercial goods labels.
As shown in figure 3, in one embodiment, the first eigenvector for obtaining at least one default shop label it
Before, method further includes step S301 and step S302:
In step S301, the shop feature vector in each shop under each default shop label is obtained；
In step s 302, according to the shop feature vector in each shop, calculate the fisrt feature of each default shop label to
Amount.
Before the first eigenvector for obtaining at least one default shop label, it can obtain each under each default shop label
The shop feature vector in shop, and the shop feature vector in each shop is used to characterize the feature of the sold commodity in each shop, Ru Gedian
Paving mainly sells which class commodity, commodity amount of all kinds of commodity etc., so, the shop based on each shop under the same shop label
Feature vector can calculate the first eigenvector of each default shop label automatically.
In one embodiment, above-mentioned steps S301 be obtain the shop feature in each shop under each default shop label to
Amount, can be performed as:
Obtain the product features vector of all commodity under each default shop label under each shop；
According to the product features vector of all commodity, the shop feature vector in each shop under each default shop label is calculated.
It, can be according under each default shop label in the shop feature vector in each shop under calculating each default shop label
Then the product features vector of all commodity under each shop integrates the product features vector of all commodity, can calculate automatically
The shop feature vector in each shop under each default shop label.
In one embodiment, according to the product features vector of all commodity, each shop under each default shop label is calculated
Shop feature vector, comprising:
According to the product features vector of all commodity under each shop, all commodity are clustered, it is poly- to obtain N number of commodity
Class；N is the positive integer more than or equal to 2.
When calculating the product features vector of each commodity under each shop, the characterization information of each commodity can be divided
Word obtains the term vector of each word, and then special according to the commodity that the term vector of each word calculates each commodity by arithmetic average method
Levy vector.This feature description information can be the detailed description information such as the name of commodity, the size of commodity, color, purposes, can also
To be the heading message of commodity.
Such as: in certain shop when commodity A entitled " mcintosh ", if vector v (red)=[1,2,3] of " red ", v
(apple)=[4,5,6], then v (A)=[2.5,3.5,4.5].
Determine the most class of commodity amount in N number of commercial articles clustering；
According to the product features vector of the commodity under the most class of commodity amount, the shop feature vector in each shop is calculated.
When calculating the shop feature vector in each shop, all commodity under each shop can be clustered, obtain N number of quotient
Product cluster, then selects the most class of commodity amount from this N number of commercial articles clustering, and with the quotient under the most class of the commodity amount
The product features vector of product, calculates the shop feature vector in each shop automatically, and commodity most can table under the most class of commodity amount
The feature in shop where levying it, such as the feature that any class commodity, the commodity mainly sold mainly are sold in the shop can be most characterized, because
And the calculation method can ensure that the accuracy of the shop feature vector in each shop.
Technical solution of the present invention is further illustrated below in conjunction with specific embodiments:
1. the use of word2vec (being the correlation that a group is used to generate term vector using all commodity title datas in micro- shop
Model) tool one word-vector model of training, obtain the term vector of all words.
2. it is flat to carry out step-by-step arithmetic using the term vector of all words by the term vector of each word is searched after each commodity participle
Come the feature vector for representing commodity, such as commodity A entitled " mcintosh ", wherein the vector v (red) of " red "=[1,
2,3], v (apple)=[4,5,6], then v (A)=[2.5,3.5,4.5]
3. all commodity under pair same shop cluster, all commodity under the most class of commodity in use quantity
Vector is averaging vector to represent feature vector (the same step 2 in shop.
4. indicating the feature vector of label using the vector weighted average in all shops under the same shop label.
5. the Euclidean distance by the feature vector of the feature vector and label that calculate the shop Wei Dian is automatically not mark
The shop of label is tagged.
Certainly, it when labelling for commodity, can calculate between the feature vector of commodity and the feature vector of shop label
Distance, and then the Commercial goods labels being adapted with shop label are automatically configured according to this distance for commodity.
Finally, it should be clear that: those skilled in the art can according to actual needs carry out above-mentioned multiple embodiments certainly
By combining.
Corresponding above-mentioned label processing method provided in an embodiment of the present invention, the embodiment of the present invention also provide a kind of tag processes
Device, as shown in figure 4, the device includes:
First obtains module 401, is configured as obtaining the first eigenvector of at least one default shop label；
Second obtains module 402, is configured as obtaining the second feature vector in the shop of label to be configured；
Determining module 403 is configured as determining the shop of label to be configured according to first eigenvector and second feature vector
The current shop label of paving.
As shown in figure 5, in one embodiment, device further include:
First computing module 501 is configured as after determining the current shop label in the shop of label to be configured, is calculated
The first distance of the third feature vector of commodity under the shop of label to be configured and current shop label；
Configuration module 502 is configured as according to first distance, corresponding for the commodity configuration under the shop of label to be configured
Commercial goods labels.
In one embodiment, device further include:
Third obtains module, is configured as before the first eigenvector for obtaining at least one default shop label, obtains
Take the shop feature vector in each shop under each default shop label；
Second computing module, is configured as the shop feature vector according to each shop, calculates the of each default shop label
One feature vector.
In one embodiment, third acquisition module includes:
Acquisition submodule, be configured as obtaining the product features of all commodity under each default shop label under each shop to
Amount；
Computational submodule is configured as the product features vector according to all commodity, calculates each under each default shop label
The shop feature vector in shop.
In one embodiment, computational submodule includes:
Cluster cell is configured as being carried out all commodity according to the product features vector of all commodity under each shop
Cluster, obtains N number of commercial articles clustering；
Determination unit is configured to determine that the most class of commodity amount in N number of commercial articles clustering；
Computing unit is configured as calculating each shop according to the product features vector of the commodity under the most class of commodity amount
The shop feature vector of paving.
Those skilled in the art will readily occur to of the invention its after considering specification and the invention invented here of practice
Its embodiment.This application is intended to cover any variations, uses, or adaptations of the invention, these modifications, purposes or
The common knowledge in the art that person's adaptive change follows general principle of the invention and do not invent including the present invention
Or conventional techniques.The description and examples are only to be considered as illustrative, and true scope and spirit of the invention are by following
Claim is pointed out.
It should be understood that the present invention is not limited to the precise structure already described above and shown in the accompanying drawings, and
And various modifications and changes may be made without departing from the scope thereof.The scope of the present invention is limited only by the attached claims.
Claims (10)
1. a kind of label processing method characterized by comprising
Obtain the first eigenvector of at least one default shop label；
Obtain the second feature vector in the shop of label to be configured；
According to the first eigenvector and the second feature vector, the current shop in the shop of the label to be configured is determined
Label.
2. the method according to claim 1, wherein the method also includes:
After determining the current shop label in the shop of the label to be configured, under the shop for calculating the label to be configured
The first distance of the third feature vector of commodity and the current shop label；
According to the first distance, corresponding Commercial goods labels are configured for the commodity under the shop of the label to be configured.
3. method according to claim 1 or 2, which is characterized in that obtaining the first of at least one default shop label
Before feature vector, the method also includes:
Obtain the shop feature vector in each shop under each default shop label；
According to the shop feature vector in each shop, the first eigenvector of each default shop label is calculated.
4. according to the method described in claim 3, it is characterized in that,
The shop feature vector for obtaining each shop under each default shop label, comprising:
Obtain the product features vector of all commodity under each default shop label under each shop；
According to the product features vector of all commodity, calculate the shop feature in each shop under each default shop label to
Amount.
5. according to the method described in claim 4, it is characterized in that,
The product features vector according to all commodity, the shop for calculating each shop under each default shop label are special
Levy vector, comprising:
According to the product features vector of all commodity under each shop, all commodity are clustered, N number of quotient is obtained
Product cluster；
Determine the most class of commodity amount in N number of commercial articles clustering；
According to the product features vector of the commodity under the most class of the commodity amount, calculate the shop feature in each shop to
Amount.
6. a kind of label processing device characterized by comprising
First obtains module, for obtaining the first eigenvector of at least one default shop label；
Second obtains module, the second feature vector in the shop for obtaining label to be configured；
Determining module, for determining the label to be configured according to the first eigenvector and the second feature vector
The current shop label in shop.
7. device according to claim 6, which is characterized in that described device further include:
First computing module, for after determining the current shop label in the shop of the label to be configured, calculate it is described to
The first distance of the third feature vector of commodity under the shop of label allocation and the current shop label；
Configuration module, for configuring corresponding quotient for the commodity under the shop of the label to be configured according to the first distance
Product label.
8. device according to claim 6 or 7, which is characterized in that described device further include:
Third obtains module, each default for obtaining before the first eigenvector for obtaining at least one default shop label
The shop feature vector in each shop under the label of shop；
Second computing module calculates the of each default shop label for the shop feature vector according to each shop
One feature vector.
9. device according to claim 8, which is characterized in that
The third obtains module
Acquisition submodule, for obtaining the product features vector of all commodity under each default shop label under each shop；
Computational submodule calculates each under each default shop label for the product features vector according to all commodity
The shop feature vector in shop.
10. device according to claim 9, which is characterized in that
The computational submodule includes:
Cluster cell carries out all commodity for the product features vector according to all commodity under each shop
Cluster, obtains N number of commercial articles clustering；
Determination unit, for determining the most class of the commodity amount in N number of commercial articles clustering；
Computing unit calculates each shop for the product features vector according to the commodity under the most class of the commodity amount
The shop feature vector of paving.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910544613.4A CN110334306A (en) | 2019-06-21 | 2019-06-21 | Label processing method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910544613.4A CN110334306A (en) | 2019-06-21 | 2019-06-21 | Label processing method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110334306A true CN110334306A (en) | 2019-10-15 |
Family
ID=68140554
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910544613.4A Pending CN110334306A (en) | 2019-06-21 | 2019-06-21 | Label processing method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110334306A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103294798A (en) * | 2013-05-27 | 2013-09-11 | 北京尚友通达信息技术有限公司 | Automatic merchandise classifying method on the basis of binary word segmentation and support vector machine |
| CN105320778A (en) * | 2015-11-25 | 2016-02-10 | 焦点科技股份有限公司 | Commodity labeling method suitable for electronic commerce Chinese website |
| US20170177596A1 (en) * | 2015-12-18 | 2017-06-22 | Bitly, Inc. | Systems and methods for benchmarking online activity via encoded links |
| CN108932648A (en) * | 2017-07-24 | 2018-12-04 | 上海宏原信息科技有限公司 | A kind of method and apparatus for predicting its model of item property data and training |
| CN108985347A (en) * | 2018-06-25 | 2018-12-11 | 阿里巴巴集团控股有限公司 | Training method, the method and device of shop classification of disaggregated model |
-
2019
- 2019-06-21 CN CN201910544613.4A patent/CN110334306A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103294798A (en) * | 2013-05-27 | 2013-09-11 | 北京尚友通达信息技术有限公司 | Automatic merchandise classifying method on the basis of binary word segmentation and support vector machine |
| CN105320778A (en) * | 2015-11-25 | 2016-02-10 | 焦点科技股份有限公司 | Commodity labeling method suitable for electronic commerce Chinese website |
| US20170177596A1 (en) * | 2015-12-18 | 2017-06-22 | Bitly, Inc. | Systems and methods for benchmarking online activity via encoded links |
| CN108932648A (en) * | 2017-07-24 | 2018-12-04 | 上海宏原信息科技有限公司 | A kind of method and apparatus for predicting its model of item property data and training |
| CN108985347A (en) * | 2018-06-25 | 2018-12-11 | 阿里巴巴集团控股有限公司 | Training method, the method and device of shop classification of disaggregated model |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107465741B (en) | Information pushing method and device | |
| CN107895181A (en) | The control method of electronic tag, apparatus and system | |
| CN104867037B (en) | A kind of data processing method and device of Figure Characteristics | |
| CN107590678A (en) | Method of Commodity Recommendation and system | |
| WO2020238363A1 (en) | Method and apparatus for pushing information, electronic commerce system and storage medium | |
| CN108595448A (en) | Information-pushing method and device | |
| CN104615721A (en) | Method and system for recommending communities based on returned goods related information | |
| WO2020236686A1 (en) | Systems and methods for color and pattern analysis of images of wearable items | |
| CN113298568B (en) | Method and device for advertising | |
| CN109146533A (en) | Information-pushing method and device | |
| CN104933601B (en) | A kind of method and apparatus exporting merchandise news | |
| CN106600360A (en) | Ordering method and device of recommending object | |
| CN108629467A (en) | A kind of sample message processing method and system | |
| CN108985839A (en) | Shopping guide method and device in unmanned supermarket based on recognition of face | |
| CN107977876B (en) | Method and device for processing order information | |
| CN109934523A (en) | Information processing method and device | |
| CN110334306A (en) | Label processing method and device | |
| CN116167825A (en) | Commodity recommendation method, device, equipment and storage medium based on electronic commerce | |
| CN104424569A (en) | Commodity price comparison method and apparatus | |
| CN109447722A (en) | A kind of merchandise valuation method of electric business platform | |
| CN111178974B (en) | Method and device for improving multi-platform fusion | |
| CN110766478A (en) | Method and device for improving user connectivity | |
| CN111639274A (en) | Online commodity intelligent sorting method and device, computer equipment and storage medium | |
| JP2017191498A (en) | Apparatus, method and program | |
| CN111507773A (en) | Coupon management method, device and storage medium |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191015 |
|
| RJ01 | Rejection of invention patent application after publication |