CN110246007A - A kind of Method of Commodity Recommendation and device - Google Patents
A kind of Method of Commodity Recommendation and device Download PDFInfo
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- CN110246007A CN110246007A CN201910452705.XA CN201910452705A CN110246007A CN 110246007 A CN110246007 A CN 110246007A CN 201910452705 A CN201910452705 A CN 201910452705A CN 110246007 A CN110246007 A CN 110246007A
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Abstract
The application provides a kind of Method of Commodity Recommendation and device, is related to communication technique field, can combine application preferences on line with StoreFront selection under line, to realize the purpose on line under guide line.This method comprises: list of targeted subscribers and the user data corresponding with target user in the list of targeted subscribers in acquisition target shop；The multidimensional user tag matrix of the target user is constructed according to the user data, the dimension of the multidimensional user tag matrix is for indicating a kind of user data, the multidimensional user tag matrix includes multiple blocks, and the block is constituted by dividing the user data of different dimensions；The criterion score of preference application is determined for all target users in pre-selection block, the pre-selection block is to screen to obtain out of the multiple block in the multidimensional user tag matrix according to preset screening item；Recommended according to preset commodity mapping library and the criterion score to the other restocking of provider, target retail shop category.
Description
Technical field
This application involves communication technique field more particularly to a kind of Method of Commodity Recommendation and device.
Background technique
With the continuous development of e-commerce, shopping online occupies very big specific gravity, line in the current consumption of people
Therefore lower solid shop/brick and mortar store increasingly declines, how to promote merchandise sales under line, its maximum value is played except main business, realizes more
The merchandise sales of more categories become current urgent problem to be solved.Current Normal practice is to be clustered by label to user, is gathered
User group is divided into several major class after class and analyzes and markets for different groups.However this mode lacks flexibility,
It is also not comprehensive enough to the analysis of user group.
Summary of the invention
The application provides a kind of Method of Commodity Recommendation and device, can mutually tie application preferences on line and StoreFront selection under line
It closes, to realize the purpose on line under guide line.
In order to achieve the above objectives, the application adopts the following technical scheme that
In a first aspect, the application provides a kind of Method of Commodity Recommendation, this method comprises:
Obtain the list of targeted subscribers and use corresponding with target user in the list of targeted subscribers in target shop
User data, the target shop are shop under line, the target user include at least one of the following: in preset time period into shop
User crosses shop user and using the target shop as the resident user in the preset range of center periphery.
The multidimensional user tag matrix of the target user, the multidimensional user tag square are constructed according to the user data
The dimension of battle array includes multiple blocks for indicating a kind of user data, the multidimensional user tag matrix, and the block is by drawing
The user data of different dimensions is divided to constitute.
The criterion score of preference application is determined for all target users in pre-selection block, the pre-selection block is basis
Preset screening item is screened from the block in the multidimensional user tag matrix to be obtained, comprising extremely in the pre-selection block
A few target user.
According to preset commodity mapping library and the criterion score to the other restocking of provider, target retail shop category
Recommend.
Second aspect, the application provide a kind of device for recommending the commodity, which includes:
Acquiring unit, for obtaining the list of targeted subscribers in target shop and being used with target in the list of targeted subscribers
The corresponding user data in family, the target shop is shop under line, when the target user includes at least one of the following: default
Between in section into shop user, cross shop user and using the target shop as the resident user in the preset range of center periphery；
Construction unit, it is described for constructing the multidimensional user tag matrix of the target user according to the user data
The dimension of multidimensional user tag matrix for indicating that a kind of user data, the multidimensional user tag matrix include multiple blocks,
The block is constituted by dividing the user data of different dimensions；
Determination unit, it is described for determining the criterion score of preference application for all target users in pre-selection block
Preselecting block is to be screened to obtain from the block in the multidimensional user tag matrix according to preset screening item, described pre-
It include at least one target user in the block of constituency；
Recommendation unit, for according to preset commodity mapping library and the criterion score to provider, the target retail shop
The other restocking of category is recommended.
The third aspect, the application provide a kind of computer readable storage medium, are stored in computer readable storage medium
Instruction, when computer executes the instruction, which, which executes in above-mentioned first aspect and its various optional implementations, appoints
Method of Commodity Recommendation described in one of meaning.
Fourth aspect, the application provides a kind of computer program product comprising instruction, when the computer program product
When running on computers so that the computer execute in above-mentioned first aspect and its various optional implementations it is any it
Method of Commodity Recommendation described in one.
5th aspect, provides a kind of device for recommending the commodity, comprising: processor and communication interface, communication interface is for described
Test device and other equipment or network communication, processor calls the program of memory storage, to execute above-mentioned first aspect institute
The Method of Commodity Recommendation stated.
The present invention provides a kind of Method of Commodity Recommendation and devices, and the user data by obtaining target area constructs multidimensional
User tag matrix determines pre-selection block according to preset screening item, and determines that all targets in the pre-selection block are used
The criterion score of family preference application, finally mentions according to preset commodity mapping library and the criterion score to the target retail shop
Restocking for merchandise classification is recommended.The program combines application preferences on line with StoreFront selection under line, realizes and instructs on line
Purpose under line solves the problems, such as that commodity restocking is recommended under line.
Detailed description of the invention
Fig. 1 is Method of Commodity Recommendation flow diagram provided by the embodiments of the present application；
Fig. 2 is the structural schematic diagram one of the device for recommending the commodity provided by the embodiments of the present application；
Fig. 3 is the structural schematic diagram two of the device for recommending the commodity provided by the embodiments of the present application.
Specific embodiment
Method of Commodity Recommendation provided by the embodiments of the present application and device are described in detail with reference to the accompanying drawing.
In the description of the present application, unless otherwise indicated, "/" indicates the meaning of "or", for example, A/B can indicate A or B.
"and/or" herein is only a kind of incidence relation for describing affiliated partner, indicates may exist three kinds of relationships, for example, A
And/or B, can indicate: individualism A exists simultaneously A and B, these three situations of individualism B.In addition, "at least one" is
Refer to one or more, " multiple " refer to two or more.
In addition, the term " includes " being previously mentioned in the description of the present application and " having " and their any deformation, it is intended that
It is to cover and non-exclusive includes.Such as the process, method, system, product or equipment for containing a series of steps or units do not have
It is defined in listed step or unit, but optionally further comprising the step of other are not listed or unit, or optionally
It further include the other step or units intrinsic for these process, methods, product or equipment.
It should be noted that in the embodiment of the present application, " illustrative " or " such as " etc. words make example, example for indicating
Card or explanation.Be described as in the embodiment of the present application " illustrative " or " such as " any embodiment or design scheme do not answer
It is interpreted than other embodiments or design scheme more preferably or more advantage.Specifically, " illustrative " or " example are used
Such as " word is intended to that related notion is presented in specific ways.
The embodiment of the present application provides a kind of Method of Commodity Recommendation, as shown in Figure 1, this method may include S101-S104:
S101, the list of targeted subscribers in target shop and corresponding with target user in the list of targeted subscribers is obtained
User data.
First according to the position acquisition operator website work in target shop ginseng, network user's signaling and business datum, so
The list of targeted subscribers in target shop and corresponding with target user in the list of targeted subscribers is filtered out by algorithm afterwards
User data, the target shop are shop under line, which includes at least one of the following: using in preset time period into shop
Shop user and using the target shop as the resident user in the preset range of center periphery is crossed at family, which includes substantially
Information and installation applicating category, which includes but is not limited to gender, age, credit grade, work and/or inhabitation
Whether ground, Income Classes enter shop, enter the shop frequency；The installation applicating category including but not limited to shopping, digital map navigation, commercial affairs are done
Public, tourism trip, game, medical treatment & health.
Specifically, obtaining user data using deep message detection (DPI) method in the present embodiment, operator is usually right
The data message for flowing through network carries out deep message detection.DPI equipment acquires network data by way of concatenating or bypassing
It flows and therefrom extracts data message, data message is parsed, extract the characteristic information in message, successively by this feature information
It is matched with the characteristic information in the feature database of storage inside it, until inquiring matched tagged word in feature database
Section, identifies data message generation according to the corresponding type of service of the characteristic information recorded in matched characteristic information and feature database
The type of service of table, DPI equipment generate discharge record after the type of service of identification data message.DPI equipment can will generate
Discharge record be sent to flow analysis platform, record data are analyzed, are dug according to specific needs by flow analysis platform
Pick.
S102, the multidimensional user tag matrix that the target user is constructed according to the user data.
User data is obtained by step S101, the multidimensional user tag square of target user is constructed according to the user data
Battle array.Specifically, carrying out labeling to each user first, the essential information of user can be divided into foundation class label and selection class
Two kinds of label, partitioning standards are the variation degree of data.
Illustratively, when target shop is to move down the line business hall, which refers to relatively-stationary user
Data, for example, gender, the age, credit grade, machine age, whether contract etc., these data once being formed, substantially will not or short
It will not change in time.Selection class label refers to continuous changed user data, such as flow grade, portfolio
Section, frequency band of changing planes, ARPU sections, when terminal contract is surplus etc..The division methods of user data include that very much, the present embodiment is only to draw
It is divided into foundation class label and selects class label as exemplary illustration, it is not limited here.
In addition it is also necessary to which preference class label is arranged according to installation applicating category, preference class label is for different type APP
Preference quantization index value, for example, specific manifestation form can be with are as follows: { { app1, index 1 }, { app2, index 2 }, { app3 refers to
Mark 3 } ... }, for difference preference using different app as classification, index value can be set to standardization day access times, enliven day by the moon
Number, daily flow etc. can specifically be selected according to different preference indexs.
Preference app relevant to target shop is only chosen in the present embodiment.User data after labeling includes foundation class
Label value, selection class label value and preference class label, i.e. { foundation class label value selects class label value, preference category to user k=
Label value }.Hyperspace matrix is constructed with foundation class label vector and selection class label vector after labeling, preference index is as value
Vector.
The dimension of the multidimensional user tag matrix is for indicating a kind of user data, such as the dimension of multidimensional user tag matrix
Degree can be the age of user, be also possible to any data that can describe user characteristics, which includes
Multiple blocks, the block are constituted by dividing the user data of different dimensions, such as when the dimension of multidimensional user tag matrix is 3
When, three dimensions can be respectively age, credit grade, daily flow.Wherein, the age can be divided as unit of 10 years old,
Credit grade can be divided into lower, medium, good, fabulous, and daily flow can be layered as unit of 500M.So the age exists
20-30 years old, credit grade was good, and user of the daily flow within the scope of 500M-1G can be divided in a block, the area
Block can be used as the basic unit of selection and marketing.
S103, the criterion score that preference application is determined for all target users in pre-selection block.
Pre-selection block is determined in the hyperspace matrix of step S102 building, which is according to preset
Screening item is screened out of multiple blocks in the multidimensional user tag matrix to be obtained.
It should be noted that the dimension of the multidimensional user tag matrix can be any data in foundation class label,
Can be selection class label in any data, according to the emphasis of research purpose can arbitrarily set with which dimension which
A little ranges include at least two as screening item, the screening item.
Such as preset screening item can for age bracket between 20-30 years old, daily flow is within the scope of 500M-1G
User can determine pre-selection block by the preset screening item, it is preferred that include at least one mesh in the pre-selection block
User is marked, if process directly terminates without target user in the pre-selection block.
For all target users fallen in the pre-selection block, can be determined according to its corresponding preference class label value
The preference application of the target user.
Specifically, obtaining all installation application types of these target users first, then pass through the maximum installation time of setting
Number threshold value and minimum installation frequency threshold value reject popular application in the installation applicating category and unexpected winner apply with determine to
Analyze set of applications.Each criterion score using i in the set of applications to be analyzed is calculated separately again, is specifically included: 1, basis
FormulaCalculate the criterion score S using i of user kki, wherein μiIndicate that institute is useful in set of applications to be analyzed
Mean value of the family about the preference class label value of application i, σiIndicate preference of all users about application i in set of applications to be analyzed
The standard deviation of class label value, v indicate the preference class label value of the user k；2, according to formulaCalculate the pre-selected zone
The criterion score using i of target complete user in block.
At least one preference application of target user in pre-selection block is finally determined according to the criterion score of each application, i.e.,
Smax=MAX { S1, S2... ..., Si}.Similarly, preference application marking can be carried out for entire target shop, and obtains target shop
Maximum preference application.
S104, according to preset commodity mapping library and the criterion score to the other restocking of provider, target retail shop category
Recommend.
The preference portrait and commodity subdivision class that user group is corresponded to based on shop, construct APP preference and classified commodity
Commodity mapping library.Commodity mapping library be by the important bridge of goods matching under preference on line and line, can in a manner of investigating needle
Questionnaire survey is carried out to consumer groups such as all ages and classes, grade, occupations, to realize that the continuous iteration of commodity mapping library updates.
The two-dimentional commodity mapping table with commodity is applied including multiple preferences in the commodity mapping library, may include multiple quotient under commodity major class
Product group, for example, commodity major class be health-care article, it includes group can be body-building clothes, fitness equipment and motion bracelet
Etc. groups, each application have corresponding mapping commodity, as shown in table 1:
Table 1
The preference application in pre-selection block or target shop is determined by step S103, then according to the preset commodity
Mapping library and the criterion score of preference application are recommended to the other restocking of provider, the target retail shop category.By inquiring commodity
Mapping library obtains preferred items list in the block.Variable grain degree can also be realized by adjusting the classification dimension of block
With the potential classified commodity list of precision, and respectively carry out user group recommend and the restocking of shop commodity.
The present invention provides a kind of Method of Commodity Recommendation and devices, and the user data by obtaining target area constructs multidimensional
User tag matrix determines pre-selection block according to preset screening item, and determines that all targets in the pre-selection block are used
The criterion score of family preference application, finally according to preset commodity mapping library and the criterion score to the provider, target retail shop
The other restocking of category is recommended.The program combines application preferences on line with StoreFront selection under line, realizes on line under guide line
Purpose, solve the problems, such as under line that commodity restocking is recommended.
Fig. 2 shows a kind of possible structural schematic diagrams of the device for recommending the commodity involved in above-described embodiment.The dress
It sets 200 and includes acquiring unit 201, construction unit 202, determination unit 203, recommendation unit 204, specific:
Acquiring unit 201, for obtain target shop list of targeted subscribers and with target in the list of targeted subscribers
The corresponding user data of user, the target shop are shop under line, which includes at least one of the following: preset time
In section into shop user, cross shop user and using the target shop as the resident user in the preset range of center periphery.
Construction unit 202, for constructing the multidimensional user tag matrix of the target user, the multidimensional according to the user data
For the dimension of user tag matrix for indicating a kind of user data, which includes multiple blocks, the block
User data by dividing different dimensions is constituted.
Determination unit 203 should for determining the criterion score of preference application for all target users in pre-selection block
Preselecting block is to be screened to obtain from the block in the multidimensional user tag matrix according to preset screening item, the pre-selected zone
It include at least one target user in block.
Recommendation unit 204, for according to preset commodity mapping library and the criterion score to the provider, target retail shop
The other restocking of category is recommended.
Optionally, which includes the essential information and installation applicating category of the target user；The essential information
Including but not limited to gender, age, credit grade, work and/or residence, Income Classes, whether enter shop, enter the shop frequency；It should
Applicating category of installing includes but is not limited to shopping, digital map navigation, business office, tourism trip, game, medical treatment & health.
The determination unit 203 is specifically used for:
The heat in the installation applicating category is rejected by the maximum installation frequency threshold value of setting and minimum installation frequency threshold value
Door application and unexpected winner are applied with determination set of applications to be analyzed；Calculate separately the mark of each application in the set of applications to be analyzed
Quasi- score；At least one preference application is determined according to the criterion score of each application.
Fig. 3 shows another possible structural schematic diagram of the device for recommending the commodity involved in above-described embodiment.It should
Device 300 includes: processor 302 and communication interface 303.Processor 302 is for carrying out control pipe to the movement of the device 300
Reason, for example, executing the step of above-mentioned construction unit 202, determination unit 203, recommendation unit 204 execute, and/or for executing sheet
Other processes of technology described in text.Communication interface 303 is used to support the communication of device 300 Yu other network entities, for example,
Execute the step of above-mentioned acquiring unit 201 executes.The device 300 can also include memory 301 and bus 304, memory 301
For storing the program code and data of the device 300.
Wherein, memory 301 can be the memory in device 300, which may include volatile memory, example
Such as random access memory；The memory also may include nonvolatile memory, such as read-only memory, flash memory,
Hard disk or solid state hard disk；The memory can also include the combination of the memory of mentioned kind.
Above-mentioned processor 302 can be realization or execute to combine and various illustratively patrols described in present disclosure
Collect box, module and circuit.The processor can be central processing unit, general processor, digital signal processor, dedicated integrated
Circuit, field programmable gate array or other programmable logic device, transistor logic, hardware component or it is any
Combination.It, which may be implemented or executes, combines various illustrative logic blocks, module and electricity described in present disclosure
Road.The processor be also possible to realize computing function combination, such as comprising one or more microprocessors combine, DSP and
The combination etc. of microprocessor.
Bus 504 can be expanding the industrial standard structure (Extended Industry Standard
Architecture, EISA) bus etc..Bus 304 can be divided into address bus, data/address bus, control bus etc..For convenient for table
Show, only indicated with a thick line in Fig. 3, it is not intended that an only bus or a type of bus.
Through the above description of the embodiments, it is apparent to those skilled in the art that, for description
It is convenienct and succinct, only the example of the division of the above functional modules, in practical application, can according to need and will be upper
It states function distribution to be completed by different functional modules, i.e., the internal structure of device is divided into different functional modules, to complete
All or part of function described above.The specific work process of the system, apparatus, and unit of foregoing description, before can referring to
The corresponding process in embodiment of the method is stated, details are not described herein.
The embodiment of the present application provides a kind of computer program product comprising instruction, when the computer program product is being counted
When being run on calculation machine, so that the computer executes Method of Commodity Recommendation described in above method embodiment.
The embodiment of the present application also provides a kind of computer readable storage medium, and finger is stored in computer readable storage medium
It enables, when the network equipment executes the instruction, which executes network in method flow shown in above method embodiment and set
The standby each step executed.
Wherein, computer readable storage medium, such as electricity, magnetic, optical, electromagnetic, infrared ray can be but not limited to or partly led
System, device or the device of body, or any above combination.The more specific example of computer readable storage medium is (non-poor
The list of act) it include: the electrical connection with one or more conducting wires, portable computer diskette, hard disk, random access memory
(Random Access Memory, RAM), read-only memory (Read-Only Memory, ROM), erasable type may be programmed read-only
It is memory (Erasable Programmable Read Only Memory, EPROM), register, hard disk, optical fiber, portable
Compact disc read-only memory (Compact Disc Read-Only Memory, CD-ROM), light storage device, magnetic memory
The computer readable storage medium of part or above-mentioned any appropriate combination or any other form well known in the art.
A kind of illustrative storage medium is coupled to processor, to enable a processor to from the read information, and can be to
Information is written in the storage medium.Certainly, storage medium is also possible to the component part of processor.Pocessor and storage media can be with
In application-specific IC (Application Specific Integrated Circuit, ASIC).In the application
In embodiment, computer readable storage medium can be any tangible medium for including or store program, which can be referred to
Enable execution system, device or device use or in connection.
The above, the only specific embodiment of the application, but the protection scope of the application is not limited thereto, it is any
Change or replacement within the technical scope of the present application should all be covered within the scope of protection of this application.Therefore, this Shen
Protection scope please should be subject to the protection scope in claims.
Claims (9)
1. a kind of Method of Commodity Recommendation characterized by comprising
The list of targeted subscribers in acquisition target shop and number of users corresponding with target user in the list of targeted subscribers
According to the target shop is shop under line, and the target user includes at least one of the following: using in preset time period into shop
Shop user is crossed and using the target shop as the resident user in the preset range of center periphery in family；
The multidimensional user tag matrix of the target user is constructed according to the user data, the multidimensional user tag matrix
Dimension includes multiple blocks for indicating a kind of user data, the multidimensional user tag matrix, and the block is by dividing not
User data with dimension is constituted；
Determine that the criterion score of preference application, the pre-selection block are according in advance for all target users in pre-selection block
The screening item of setting is screened from the block in the multidimensional user tag matrix to be obtained, and includes at least one in the pre-selection block
A target user；
Recommended according to preset commodity mapping library and the criterion score to the other restocking of provider, target retail shop category.
2. Method of Commodity Recommendation according to claim 1, which is characterized in that the user data includes the target user
Essential information and installation applicating category；
The essential information include but is not limited to gender, age, credit grade, work and/or residence, Income Classes, whether
Enter shop, enter the shop frequency；
The installation applicating category including but not limited to shopping, digital map navigation, business office, tourism trip, game, medical treatment is strong
Health.
3. -2 described in any item Method of Commodity Recommendation according to claim 1, which is characterized in that described in pre-selection block
All target users determine that the criterion score of preference application includes:
The hot topic in the installation applicating category is rejected by the maximum installation frequency threshold value of setting and minimum installation frequency threshold value
Using and unexpected winner apply with determination set of applications to be analyzed；
Calculate separately the criterion score of each application in the set of applications to be analyzed；
At least one preference application is determined according to the criterion score of each application.
4. a kind of device for recommending the commodity characterized by comprising
Acquiring unit, for obtain target shop list of targeted subscribers and with target user's phase in the list of targeted subscribers
Corresponding user data, the target shop are shop under line, and the target user includes at least one of the following: preset time period
It is interior into shop user, cross shop user and using the target shop as the resident user in the preset range of center periphery；
Construction unit, for constructing the multidimensional user tag matrix of the target user, the multidimensional according to the user data
For the dimension of user tag matrix for indicating a kind of user data, the multidimensional user tag matrix includes multiple blocks, described
Block is constituted by dividing the user data of different dimensions；
Determination unit, for determining the criterion score of preference application, the pre-selection for all target users in pre-selection block
Block is to be screened to obtain from the block in the multidimensional user tag matrix according to preset screening item, the pre-selected zone
It include at least one target user in block；
Recommendation unit, for according to preset commodity mapping library and the criterion score to provider, target retail shop category
Other restocking is recommended.
5. the device for recommending the commodity according to claim 4, which is characterized in that the user data includes the target user
Essential information and installation applicating category；
The essential information include but is not limited to gender, age, credit grade, work and/or residence, Income Classes, whether
Enter shop, enter the shop frequency；
The installation applicating category including but not limited to shopping, digital map navigation, business office, tourism trip, game, medical treatment is strong
Health.
6. according to the described in any item devices for recommending the commodity of claim 4-5, which is characterized in that the determination unit is specifically used
In:
The hot topic in the installation applicating category is rejected by the maximum installation frequency threshold value of setting and minimum installation frequency threshold value
Using and unexpected winner apply with determination set of applications to be analyzed；
Calculate separately the criterion score of each application in the set of applications to be analyzed；
At least one preference application is determined according to the criterion score of each application.
7. a kind of device for recommending the commodity, which is characterized in that described device includes: processor, memory and communication interface, described logical
Interface is believed for described device and other equipment or network communication, and memory calls memory to deposit for storing program, processor
The program of storage, with execute as claim 1-3 arbitrarily one of as described in Method of Commodity Recommendation.
8. a kind of computer readable storage medium, it is stored with instruction in computer readable storage medium, is referred to when computer executes this
When enabling, the computer execute it is one of any in the claims 1-3 described in Method of Commodity Recommendation.
9. a kind of computer program product comprising instruction, when the computer program product is run on computers, the meter
Calculation machine execute it is one of any in the claims 1-3 described in Method of Commodity Recommendation.
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| CN110602532A (en) * | 2019-09-24 | 2019-12-20 | 腾讯科技（深圳）有限公司 | Entity article recommendation method, device, server and storage medium |
| CN111986005A (en) * | 2020-08-31 | 2020-11-24 | 上海博泰悦臻电子设备制造有限公司 | Activity recommendation method and related equipment |
| CN112036988A (en) * | 2020-09-24 | 2020-12-04 | 上海风秩科技有限公司 | Label generation method and device, storage medium and electronic equipment |
| CN112182421A (en) * | 2020-09-23 | 2021-01-05 | 支付宝(杭州)信息技术有限公司 | Potential customer mining method and device and electronic equipment |
| CN112231576A (en) * | 2020-11-05 | 2021-01-15 | 中国联合网络通信集团有限公司 | Target user determination method and device |
| CN112508439A (en) * | 2020-12-18 | 2021-03-16 | 威海天鑫现代服务技术研究院有限公司 | Data analysis method, system, computer equipment and storage medium |
| CN112613953A (en) * | 2020-12-29 | 2021-04-06 | 北京环球国广媒体科技有限公司 | Commodity selection method, system and computer readable storage medium |
| CN113657951A (en) * | 2020-05-12 | 2021-11-16 | 阿里巴巴集团控股有限公司 | Commodity recommendation method and device, and commodity release processing method and device |
| CN113674065A (en) * | 2021-08-30 | 2021-11-19 | 中国平安人寿保险股份有限公司 | Service contact-based service recommendation method and device, electronic equipment and medium |
| CN113947015A (en) * | 2021-09-27 | 2022-01-18 | 上海维智卓新信息科技有限公司 | Method and device for determining goods on shelves |
| TWI812209B (en) * | 2021-05-13 | 2023-08-11 | 日商樂天集團股份有限公司 | Information processing system, information processing method and program product |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160086232A1 (en) * | 2014-09-19 | 2016-03-24 | International Business Machines Corporation | Advertising within social networks |
| CN105630946A (en) * | 2015-12-23 | 2016-06-01 | 百度在线网络技术（北京）有限公司 | Big data based field cross recommendation method and apparatus |
| CN109359244A (en) * | 2018-10-30 | 2019-02-19 | 中国科学院计算技术研究所 | A kind of recommendation method for personalized information and device |
-
2019
- 2019-05-28 CN CN201910452705.XA patent/CN110246007B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160086232A1 (en) * | 2014-09-19 | 2016-03-24 | International Business Machines Corporation | Advertising within social networks |
| CN105630946A (en) * | 2015-12-23 | 2016-06-01 | 百度在线网络技术（北京）有限公司 | Big data based field cross recommendation method and apparatus |
| CN109359244A (en) * | 2018-10-30 | 2019-02-19 | 中国科学院计算技术研究所 | A kind of recommendation method for personalized information and device |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110602532A (en) * | 2019-09-24 | 2019-12-20 | 腾讯科技（深圳）有限公司 | Entity article recommendation method, device, server and storage medium |
| CN113657951A (en) * | 2020-05-12 | 2021-11-16 | 阿里巴巴集团控股有限公司 | Commodity recommendation method and device, and commodity release processing method and device |
| CN111986005A (en) * | 2020-08-31 | 2020-11-24 | 上海博泰悦臻电子设备制造有限公司 | Activity recommendation method and related equipment |
| CN112182421A (en) * | 2020-09-23 | 2021-01-05 | 支付宝(杭州)信息技术有限公司 | Potential customer mining method and device and electronic equipment |
| CN112182421B (en) * | 2020-09-23 | 2024-05-17 | 支付宝(杭州)信息技术有限公司 | Potential customer mining method and device and electronic equipment |
| CN112036988B (en) * | 2020-09-24 | 2023-04-07 | 上海风秩科技有限公司 | Label generation method and device, storage medium and electronic equipment |
| CN112036988A (en) * | 2020-09-24 | 2020-12-04 | 上海风秩科技有限公司 | Label generation method and device, storage medium and electronic equipment |
| CN112231576A (en) * | 2020-11-05 | 2021-01-15 | 中国联合网络通信集团有限公司 | Target user determination method and device |
| CN112231576B (en) * | 2020-11-05 | 2023-06-06 | 中国联合网络通信集团有限公司 | Target user determination method and device |
| CN112508439A (en) * | 2020-12-18 | 2021-03-16 | 威海天鑫现代服务技术研究院有限公司 | Data analysis method, system, computer equipment and storage medium |
| CN112613953A (en) * | 2020-12-29 | 2021-04-06 | 北京环球国广媒体科技有限公司 | Commodity selection method, system and computer readable storage medium |
| TWI812209B (en) * | 2021-05-13 | 2023-08-11 | 日商樂天集團股份有限公司 | Information processing system, information processing method and program product |
| CN113674065B (en) * | 2021-08-30 | 2023-09-22 | 中国平安人寿保险股份有限公司 | Service contact-based service recommendation method and device, electronic equipment and medium |
| CN113674065A (en) * | 2021-08-30 | 2021-11-19 | 中国平安人寿保险股份有限公司 | Service contact-based service recommendation method and device, electronic equipment and medium |
| CN113947015A (en) * | 2021-09-27 | 2022-01-18 | 上海维智卓新信息科技有限公司 | Method and device for determining goods on shelves |
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