CN105022729B - A kind of user preference determines method and device - Google Patents
A kind of user preference determines method and device Download PDFInfo
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- CN105022729B CN105022729B CN201410150723.XA CN201410150723A CN105022729B CN 105022729 B CN105022729 B CN 105022729B CN 201410150723 A CN201410150723 A CN 201410150723A CN 105022729 B CN105022729 B CN 105022729B
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Abstract
The invention discloses a kind of user preference to determine method, including：Determine that user accesses number of each type of service in preset time period in taxonomic hierarchies, determine that user accesses the number preference of each business in taxonomic hierarchies using H exponentiation algorithms；Or determine that user accesses flowing of access of each business in the preset time period in taxonomic hierarchies, determine that user accesses the flow preference of each business in taxonomic hierarchies using H exponentiation algorithms；Determine the type of service in the taxonomic hierarchies that user accesses for the first time daily, and in preset time period the daily first time access service type of counting user first total number of days, determine that what user accessed each business in taxonomic hierarchies browses preference using H exponentiation algorithms；Second total number of days of each type of service in preset time in user's connected reference taxonomic hierarchies is determined, determines that user accesses the type of service preference of each business in taxonomic hierarchies using H exponentiation algorithms.The invention also discloses a kind of user preference determining device.
Description
Technical field
The present invention relates to business support technology, more particularly to a kind of user preference to determine method and device.
Background technology
With the rapid popularization of developing rapidly for mobile Internet, and smart mobile phone, the main body of existing customer is just from logical
Believe that client changes to Internet subscribers, customer demand has been changed into the internet demand of personalization by single communication requirement, is
The change in market and the change of customer demand are complied with, it is necessary on the basis of tradition is through divided data, type of service and flow etc. are entered
Row depth analysis, deeply sees clearly user's request, the final construction for effectively promoting intelligent pipeline, lifts flow scale and income.
The content of the invention
To solve existing technical problem, the embodiments of the invention provide a kind of user preference to determine method and dress
Put, business of the operator more accurately to user's recommended user's preference can be made.
What the technical scheme of the embodiment of the present invention was realized in：The embodiment of the present invention provides a kind of user preference determination side
Method, methods described include：
Determine that user accesses number of each type of service in preset time period in taxonomic hierarchies, will using H exponentiation algorithms
The number is ranked up, and determines that user accesses the number preference of each business in taxonomic hierarchies；Or determine that user accesses and divide
Flowing of access of each business in the preset time period in class system, flowing of access is ranked up using H exponentiation algorithms, really
Determine the flow preference that user accesses each business in taxonomic hierarchies；
The type of service in the taxonomic hierarchies that user accesses for the first time daily is determined, and is counted in the preset time period
The user accesses first total number of days of the type of service for the first time daily, is carried out first total number of days using H exponentiation algorithms
Sequence, determine that user accesses each business in taxonomic hierarchies and browses preference；
Second total number of days of each type of service in the preset time in user's connected reference taxonomic hierarchies is determined, is used
Second total number of days is ranked up by H exponentiation algorithms, determines that user accesses the type of service preference of each business in taxonomic hierarchies.
Further, methods described also includes：
Respectively described number preference, preference and type of service preference setting weight are browsed, it is true according to weight
The fixed number preference, the weighted average for browsing preference and type of service preference, by identified weighted average
Value is used as number preference coefficient.
Further, methods described also includes：
Respectively described flow preference, preference and type of service preference setting weight are browsed, it is true according to weight
The fixed flow preference, the weighted average for browsing preference and type of service preference, by identified weighted average
Value is used as flow preference coefficient.
Further, methods described also includes：
The stream used in determining the user in N number of continuous preset time period respectively in each preset time period
Amount；
Determine that flow stepping identifies, and determine respectively residing for the flow that the user uses in each preset time period
Flow stepping identifies；
Flow stepping mark residing for the flow used according to the user in each preset time period determines the use
The class letter at family.
Further, methods described also includes：
According to the number preference, flow preference, the grade mark for browsing preference, type of service preference and user
Know the business for recommending the user preference for the user.
The embodiment of the present invention also provides a kind of user preference determining device, including：
First determining unit, for determining that user accesses time of each type of service in preset time period in taxonomic hierarchies
The number, is ranked up by number using H exponentiation algorithms, determines that user accesses the number preference of each business in taxonomic hierarchies；
Or determine that user accesses flowing of access of each business in the preset time period in taxonomic hierarchies, will using H exponentiation algorithms
Flowing of access is ranked up, and determines that user accesses the flow preference of each business in taxonomic hierarchies；
Second determining unit, the type of service in the taxonomic hierarchies accessed for the first time daily for determining user, and in institute
First total number of days that the statistics user in preset time period accesses the type of service for the first time daily is stated, is calculated using H indexes
First total number of days is ranked up by method, determines that user accesses each business in taxonomic hierarchies and browses preference；
3rd determining unit, for determining each type of service in the preset time in user's connected reference taxonomic hierarchies
Second total number of days, using H exponentiation algorithms second total number of days is ranked up, determines that user accesses each business in taxonomic hierarchies
Type of service preference.
Further, described device also includes：
Setting unit, for be respectively the number preference, browse preference and type of service preference set power
Weight；
Computing unit, for the number preference according to setting, browse preference and type of service preference
Weight determines the number preference, browses the weighted average of preference and type of service preference, identified will add
Weight average value is as number preference coefficient.
Further, the setting unit, it is additionally operable to set weight for the flow preference, and again to be described clear
Looking at preference and type of service preference sets weight；Accordingly,
The computing unit, it is additionally operable to the flow preference according to setting, browses preference and type of service is inclined
The weight spent well determines the flow preference, browses the weighted average of preference and type of service preference, will determine
Weighted average as flow preference coefficient.
Further, described device also includes：
4th determining unit, for each default in determining the user in N number of continuous preset time period respectively
The flow used in period；
5th determining unit, for determining that flow stepping identifies, and determine the user in each preset time period respectively
Flow stepping mark residing for the flow inside used；
6th determining unit, for the flow point residing for the flow that is used according to the user in each preset time period
Shelves mark determines the class letter of the user.
Further, described device also includes：Recommendation unit, for according to the number preference, flow preference, clear
The class letter of preference, type of service preference and user look at recommends the business of the user preference for the user.
The user preference that the embodiment of the present invention is provided determines method and device, can be referred to by the taxonomic hierarchies and H of refinement
Method is figured, determines the type of service of user preference, accurately to recommend business to lay a good foundation to user, marketing is improved and pushes away
The wide degree of accuracy；
In addition, present invention method can combine user tag storehouse, identification in time using competing product user and
The type of service that the user uses, and the type of service used according to the user and the user and the battalion of operator
Pin activity etc., business of the triggering operator to the user recommended user preference.
Brief description of the drawings
Fig. 1 is the implementation process schematic diagram that user preference of the embodiment of the present invention determines method；
Fig. 2 is the structural representation one of user preference determining device of the embodiment of the present invention；
Fig. 3 is the structural representation two of user preference determining device of the embodiment of the present invention；
Fig. 4 is the structural representation three of user preference determining device of the embodiment of the present invention.
Embodiment
In order to more fully hereinafter understand the features of the present invention and technology contents, below in conjunction with the accompanying drawings to the reality of the present invention
Now it is described in detail, appended accompanying drawing purposes of discussion only for reference, is not used for limiting the present invention.
Fig. 1 is the implementation process schematic diagram that user preference of the embodiment of the present invention determines method, as shown in figure 1, methods described
Including：
Step 101：Determine that user accesses number of each type of service in preset time period in taxonomic hierarchies, referred to using H
Figure method to be ranked up the number, determine that user accesses the number preference of each business in taxonomic hierarchies；Or it is determined that with
Family accesses flowing of access of each business in the preset time period in taxonomic hierarchies, is entered flowing of access using H exponentiation algorithms
Row sequence, determine that user accesses the flow preference of each business in taxonomic hierarchies；
Here, the H exponentiation algorithms be according to the height of the access times of type of service come the user that determines to the industry
The method of the preference of service type；Accordingly, it is described to be ranked up the number using H exponentiation algorithms, determine that user accesses
The number preference of each business, is specifically included in taxonomic hierarchies：
For each type of service in taxonomic hierarchies, user is accessed into the type of service in taxonomic hierarchies respectively
Number be ranked up from big to small, and line label is entered from big to small to the number, use corresponding to h label before and if only if
The number that family accesses the type of service in taxonomic hierarchies is at least h, meanwhile, user corresponding to h+1 labels accesses taxonomic hierarchies
In the number of type of service when being less than h+1, the number preference that the user accesses the type of service in taxonomic hierarchies is h；
Different business in corresponding taxonomic hierarchies, it can determine that user accesses time of each business according to the above method
Number preference；Wherein, the H indexes are higher, and user preference degree is higher.
Similarly, the flow preference, the determination method for browsing preference and type of service preference and described time
The determination method of number preference is similar, therefore repeats no more.
Step 102：The type of service in the taxonomic hierarchies that user accesses for the first time daily is determined, and in the preset time
The statistics user accesses first total number of days of the type of service for the first time daily in section, total by first using H exponentiation algorithms
Number of days is ranked up, and determines that user accesses each business in taxonomic hierarchies and browses preference；
Step 103：Determine the second total of each type of service in the preset time in user's connected reference taxonomic hierarchies
Number of days, second total number of days is ranked up using H exponentiation algorithms, determines that user accesses the type of service of each business in taxonomic hierarchies
Preference.
Here, the execution sequence of step 102 to step 103 can be exchanged.
Further, methods described also includes：
Respectively described number preference, preference and type of service preference setting weight are browsed, it is true according to weight
The fixed number preference, the weighted average for browsing preference and type of service preference, by identified weighted average
Value is used as number preference coefficient.
Further, methods described also includes：
Respectively described flow preference, preference and type of service preference setting weight are browsed, it is true according to weight
The fixed flow preference, the weighted average for browsing preference and type of service preference, by identified weighted average
Value is used as flow preference coefficient.
Here, if the number preference of certain class business of user, flow preference, browsing preference and type of service preference
Any value in degree is sky, then null value is replaced with into a fixed value, subsequent calculation times preference coefficient and flow preference coefficient；
Such as, however, it is determined that user A B types of service browse preference as sky, then by corresponding to the B industry of the user A
The preference that browses of business replaces with 1000, it is later determined that the number preference coefficient of the user A：
I.e. described number preference coefficient
Wherein, the C is the number preference of user A B types of service, and the D is the industry of user A B types of service
Service type preference；Described X1, Y1 and Z1 are corresponding to the number preference, browse preference and type of service preference
Weight, and described X1, Y1 and Z1 are empirical value.
Here, if number preference coefficient is bigger corresponding to the B types of service of the user A, illustrate that the user A gets over preference
The B types of service；If flow preference coefficient is bigger corresponding to the B types of service of the user A, illustrate the user A in institute
It is more to state the flow spent in B types of service, i.e., B types of service more described in preference.
Further, methods described also includes：
The stream used in determining the user in N number of continuous preset time period respectively in each preset time period
Amount；
Determine that flow stepping identifies, and determine respectively residing for the flow that the user uses in each preset time period
Flow stepping identifies；
Flow stepping mark residing for the flow used according to the user in each preset time period determines the use
The class letter at family.
Here, the N can specifically be set according to actual conditions, typically greater than the positive integer equal to 2；When described default
Between section can arbitrarily set according to the actual requirements, be such as arranged to one month, determine number preference, the stream of user in one month
Amount preference, preference, type of service preference, number preference coefficient and flow preference coefficient are browsed, meanwhile, according to continuous N
The flow stepping used the mark of user in individual month, determines the class letter of user, such as high consumption group or low consumption group, enters
And the type of service according to the preference of user and the class letter of user for user's recommended user's preference, in this way, operator can be made
More accurately to the business of user's recommended user's preference.
Further, methods described also includes：
According to the number preference, flow preference, the grade mark for browsing preference, type of service preference and user
Know the business for recommending the user preference for the user.
Here, it is described to determine that data involved in method are the full dose data in taxonomic hierarchies for preference, that is, use
Number, flowing of access, total number of days of first time access service type and the connected reference type of service of family access service type
Total number of days be full dose data in taxonomic hierarchies.
Here, the division of classification can be classified by establishing classification tree with reference to own type of service in the taxonomic hierarchies
Method, and with reference to Internet service genre classification methods etc., business is sorted out from different perspectives；Meanwhile for specific
Business, the content being related in business can be further divided into each detailed subclass as needed, for example will read into one
Step is divided into city, science fiction, through the subclass such as pipe and history；In addition, the division of classification also needs to take into account different agreement in taxonomic hierarchies, i.e.,
Consider HTTP and non-http protocol simultaneously, the business for being easy to flexibly use each agreement is segmented as far as possible, is accurately to all kinds of
The business of user's recommended user's preference lays the foundation.
Here, the embodiment of the present invention can number of pass times preference, flow preference, to browse preference, type of service inclined
The class letter of good degree, number preference coefficient, flow preference coefficient and user determines preference of the user to certain class business, and
The higher business of user's frequency of use is determined, so, it is possible to make operator according to user preference more accurately to user be recommended
The business of user preference.
In addition, it be can determine using the embodiment of the present invention in a period, the feelings that user migrates to the product of rival
Condition, user is planned to a factor of own service to the situation that the product of rival migrates as measurement operator, favorably
User situation in operator's analysis using competing product, to plan the battalion of operator according to the user situation using competing product
Pin strategy；Simultaneously as the excavation of the competing product of each business needs to regularly update, therefore, last word will be manually paid close attention to
The user preference that dynamic and the embodiment of the present invention are dynamically determined is combined, and can enrich analysis identification user preference business model
Storehouse.
The embodiment of the present invention additionally provides a kind of user preference determining device, as shown in Fig. 2 shown device includes：
First determining unit 21, for determining that user accesses time of each type of service in preset time period in taxonomic hierarchies
The number, is ranked up by number using H exponentiation algorithms, determines that user accesses the number preference of each business in taxonomic hierarchies；
Or determine that user accesses flowing of access of each business in the preset time period in taxonomic hierarchies, will using H exponentiation algorithms
Flowing of access is ranked up, and determines that user accesses the flow preference of each business in taxonomic hierarchies；
Second determining unit 22, the type of service in the taxonomic hierarchies accessed for the first time daily for determining user, and
The statistics user accesses first total number of days of the type of service for the first time daily in the preset time period, using H indexes
First total number of days is ranked up by algorithm, determines that user accesses each business in taxonomic hierarchies and browses preference；
3rd determining unit 23, for determining each service class in the preset time in user's connected reference taxonomic hierarchies
Second total number of days of type, second total number of days is ranked up using H exponentiation algorithms, determines that user accesses each business in taxonomic hierarchies
Type of service preference.
Based on the device shown in Fig. 2, as shown in figure 3, described device also includes：
Setting unit 24, for be respectively the number preference, browse preference and type of service preference set
Weight；
Computing unit 25, for the number preference according to setting, browse preference and type of service preference
Weight determine the number preference, browse the weighted average of preference and type of service preference, will determined by
Weighted average is as number preference coefficient.
Further, the setting unit 24 is additionally operable to set weight for the flow preference, and is described again
Browse preference and type of service preference sets weight；Accordingly,
The computing unit 25, it is additionally operable to the flow preference according to setting, browses preference and type of service
The weight of preference determines the flow preference, browses the weighted average of preference and type of service preference, will be true
Fixed weighted average is as flow preference coefficient.
Based on the device shown in Fig. 3, as shown in figure 4, described device also includes：4th determining unit 26, for true respectively
The flow used in determining the user in N number of continuous preset time period in each preset time period；
5th determining unit 27, for determining that flow stepping identifies, and determine the user in each preset time respectively
Flow stepping mark residing for the flow used in section；
6th determining unit 28, for the flow residing for the flow that is used according to the user in each preset time period
Stepping mark determines the class letter of the user.
Recommendation unit 29, for according to the number preference, flow preference, browse preference, type of service preference
Degree and the class letter of user recommend the business of the user preference for the user.
First determining unit, the second determining unit, the 3rd determining unit, setting unit, computing unit, the 4th determine
Unit, the 5th determining unit, the 6th determining unit and recommendation unit can be run on computer, can be by computer
Central processing unit（CPU）Or microprocessor（MPU）Or digital signal processor（DSP）Or programmable gate array（FPGA）
Realize.
The foregoing is only a specific embodiment of the invention, but protection scope of the present invention is not limited thereto, any
Those familiar with the art the invention discloses technical scope in, change or replacement can be readily occurred in, should all be contained
Cover within protection scope of the present invention.Therefore, protection scope of the present invention should be based on the protection scope of the described claims.
The foregoing is only a preferred embodiment of the present invention, is not intended to limit the scope of the present invention.
Claims (8)
1. a kind of user preference determines method, it is characterised in that methods described includes：
Determine that user accesses number of each type of service in preset time period in taxonomic hierarchies, using H exponentiation algorithms by described in
Number is ranked up, and determines that user accesses the number preference of each business in taxonomic hierarchies；Or determine that user accesses classified body
Flowing of access of each business in the preset time period in system, flowing of access is ranked up using H exponentiation algorithms, it is determined that with
Family accesses the flow preference of each business in taxonomic hierarchies；
The type of service in the taxonomic hierarchies that user accesses for the first time daily is determined, and in the preset time period described in statistics
User accesses first total number of days of the type of service for the first time daily, is ranked up first total number of days using H exponentiation algorithms,
Determine that user accesses each business in taxonomic hierarchies and browses preference；
Second total number of days of each type of service in the preset time in user's connected reference taxonomic hierarchies is determined, is referred to using H
Figure method to be ranked up second total number of days, determine that user accesses the type of service preference of each business in taxonomic hierarchies；
According to the number preference, browse preference and type of service preference, or according to the flow preference, browse
Preference and type of service preference are the business that the user recommends the user preference.
2. according to the method for claim 1, it is characterised in that methods described also includes：
Respectively described number preference, preference and type of service preference setting weight are browsed, institute is determined according to weight
State number preference, browse the weighted average of preference and type of service preference, identified weighted average is made
For number preference coefficient；
It is the business that the user recommends the user preference according to the number preference coefficient.
3. according to the method for claim 1, it is characterised in that methods described also includes：
Respectively described flow preference, preference and type of service preference setting weight are browsed, institute is determined according to weight
State flow preference, browse the weighted average of preference and type of service preference, identified weighted average is made
For flow preference coefficient；
It is the business that the user recommends the user preference according to the flow preference coefficient.
4. according to the method described in any one of claims 1 to 3, it is characterised in that methods described also includes：
The flow used in determining the user in N number of continuous preset time period respectively in each preset time period；
Determine that flow stepping identifies, and determine the flow residing for the flow that the user uses in each preset time period respectively
Stepping identifies；
Flow stepping mark residing for the flow used according to the user in each preset time period determines the user's
Class letter；
It is the business that the user recommends the user preference according to the class letter of the user.
5. a kind of user preference determining device, it is characterised in that described device includes：
First determining unit, for determining that user accesses number of each type of service in preset time period in taxonomic hierarchies, adopt
The number is ranked up with H exponentiation algorithms, determines that user accesses the number preference of each business in taxonomic hierarchies；Or
Determine that user accesses flowing of access of each business in the preset time period in taxonomic hierarchies, will be accessed using H exponentiation algorithms
Flow is ranked up, and determines that user accesses the flow preference of each business in taxonomic hierarchies；
Second determining unit, the type of service in the taxonomic hierarchies accessed for the first time daily for determining user, and described pre-
, will using H exponentiation algorithms if the statistics user accesses first total number of days of the type of service for the first time daily in the period
First total number of days is ranked up, and determines that user accesses each business in taxonomic hierarchies and browses preference；
3rd determining unit, for determining of each type of service in the preset time in user's connected reference taxonomic hierarchies
Two total number of days, second total number of days is ranked up using H exponentiation algorithms, determines that user accesses the business of each business in taxonomic hierarchies
Type preference；
According to the number preference, browse preference and type of service preference, or according to the flow preference, browse
Preference and type of service preference are the business that the user recommends the user preference.
6. device according to claim 5, it is characterised in that described device also includes：
Setting unit, for be respectively the number preference, browse preference and type of service preference set weight；
Computing unit, for the number preference according to setting, browse the weight of preference and type of service preference
Determine the number preference, browse the weighted average of preference and type of service preference, identified weighting is flat
Average is as number preference coefficient；
It is the business that the user recommends the user preference according to the number preference coefficient.
7. device according to claim 6, it is characterised in that the setting unit, be additionally operable to as the flow preference
Weight is set, and browses preference and type of service preference setting weight again to be described；Accordingly,
The computing unit, it is additionally operable to the flow preference according to setting, browses preference and type of service preference
Weight determine the flow preference, browse the weighted average of preference and type of service preference, by determination plus
Weight average value is as flow preference coefficient；
It is the business that the user recommends the user preference according to the flow preference coefficient.
8. according to the device described in any one of claim 5 to 7, it is characterised in that described device also includes：
4th determining unit, for each preset time in determining the user in N number of continuous preset time period respectively
The flow used in section；
5th determining unit, for determining that flow stepping identifies, and determine that the user makes in each preset time period respectively
Flow stepping mark residing for flow；
6th determining unit, for the flow stepping mark residing for the flow that is used according to the user in each preset time period
Know the class letter for determining the user；
It is the business that the user recommends the user preference according to the class letter of the user.
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| CN106066864B (en) * | 2016-05-27 | 2019-04-30 | 重庆邮电大学 | A kind of various dimensions mobile subscriber preference dynamic identifying method |
| CN107451140B (en) * | 2016-05-30 | 2020-08-25 | 阿里巴巴集团控股有限公司 | Method and device for determining user preference degree |
| CN108629608B (en) * | 2017-03-22 | 2023-02-24 | 腾讯科技（深圳）有限公司 | User data processing method and device |
| CN107169825A (en) * | 2017-05-08 | 2017-09-15 | 浙江敢尚网络科技有限公司 | A kind of user preference analysis method and system |
| CN107995289B (en) * | 2017-12-01 | 2019-08-06 | 中国联合网络通信集团有限公司 | A kind of the behaviortrace method and platform of the network user |
| CN110796505B (en) * | 2018-08-03 | 2023-07-04 | 淘宝（中国）软件有限公司 | Business object recommendation method and device |
| CN111093251B (en) * | 2019-12-11 | 2021-03-16 | 深圳市高德信通信股份有限公司 | Internet access selection system based on user requirements |
| CN112333040B (en) * | 2020-10-12 | 2023-05-02 | 联通智网科技股份有限公司 | Flow separation method and device, storage medium and computer equipment |
| CN115129725A (en) * | 2021-03-25 | 2022-09-30 | 北京沃东天骏信息技术有限公司 | User classification method and device, storage medium and electronic equipment |
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