CN102609786B - Method and device for forecasting whether user is off network - Google Patents
Method and device for forecasting whether user is off network Download PDFInfo
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Abstract
The invention discloses a method for forecasting whether a user is off network, which comprises the following steps: distributing matrix-type site elements to a registered user who logs in; setting the initial value of each element; recording all the operations of the registered user; summarizing the operations of the registered user; calculating an average active value of the user; judging whether the average active values of different users in the preset time present descending trend or not, if so, determining the user to be an off-network user; otherwise, returning to the step of distributing matrix-type site elements to the registered user who logs in, and setting the initial value of each element. The invention also discloses a device for forecasting whether the user is off network. The invention provides a user behavior excavation manner, which can be used for calculating the standard active value by matching with a certain algorithm, thereby forecasting the possibility of the user off network in advance from the trend of the active degree of the user, so that operators can take corresponding remedial measures.
Description
Technical field
The present invention relates to Internet technical field, more particularly, it relates to a kind of prediction off-grid method of user, device.
Background technology
With the continuous development of social informatization and network, website operation personnel usually it should be understood that user and website
The behavior showing in interaction.
Current social network sites off-network analysis, is usually judged using the User logs in time, such as certain user exceedes
It is not logged within three months it can be determined that this user off-network.But this mode has a drawback, although substantially may be used first
To judge this user off-network, but other behaviors of this user we unpredictable arrive, such as user may throw
To other websites；Or fall in love with fresher things so as to not interested to the original content browsing；Or this
User because website a certain function he especially do not like, thus leaving, these are also to count.
The chain that existing telecommunications industry commonly uses excavates mode, carrys out digging user internet behavior, is only suitable for flat business
Framework is it is impossible to meet the multidimensional operation mode of social network sites user.
Content of the invention
In order to solve above technical problem, the invention provides a kind of prediction off-grid method and apparatus of user.
The invention discloses a kind of prediction off-grid method of user, including：
Distribution moments configuration site element table gives the register user logging in, the initial value of setting each element；
Record all of operation of described register user；
Described register user operation is collected, is calculated user's average active value；
Judge in predetermined amount of time, whether multiple different user's average active values are in downward trend, if being defined as
Off-network purpose user, if it is not, then the register user logging in given by return to step distribution moments configuration site element table, setting each element
Initial value；
Wherein, described matrix form site element table includes laterally arranging and longitudinal row, and the described user behavior that is laterally classified as divides
Class, described longitudinal direction is classified as at least one website function sets classification.
Preferably, described user's average active value calculating method is：If user's average active value is Hd, wherein d is the date,
If ∑ is element value sum, if T is the valuation ∑ of all users being calculated using average algorithm, if n is user operation
Number, then
Preferably, also include the initial value of the element in reset site element table.
Preferably, described calculating user's average active value includes：Network is the server distribution of many clusters, needs first to converge
Total data, is then calculated again.
Preferably, described user behavior classification includes：Browse, deliver, comment on, share, delete；Described website function
Sets classification includes：Strange thing, photograph album, daily record, good friend, BLOG.
A kind of prediction off-grid device of user, for said method, including：
List of elements allocation unit, gives, for distribution moments configuration site element table, the register user logging in, and sets each element value
Initial value；
User operation records unit, is connected with described list of elements allocation unit, for recording the institute of described register user
Some operations；
Average active value computing unit, is connected with described user operation records unit, for by described register user
Operation is collected, and calculates user's average active value and preserves；
User behavior Trend judgement unit, is connected with described average active value computing unit, judges in predetermined amount of time
Whether multiple different user's average active values are in downward trend；
Wherein, described matrix form site element table includes laterally arranging and longitudinal row, and described is laterally classified as user behavior
Classification, described longitudinal direction is classified as at least one website function sets classification.
Preferably, described average active value computing unit adopts equation below to calculate user's average active value Hd：Wherein d is
Date, if ∑ is element value sum, if T is the valuation ∑ of all users being calculated using average algorithm, if n is user
Operation number, then
Preferably, also include the unit being arranged between described average active value computing unit and downward trend judging unit
Element value reset unit, for the initial value of the element of the site element table that resets.
Preferably, described calculating user's average active value includes：Network is the server distribution of many clusters, needs first to converge
Total data, is then calculated again.
Preferably, described user behavior classification includes：Browse, deliver, comment on, share, delete；Described website function
Sets classification includes：Strange thing, photograph album, daily record, good friend, BLOG.
Implement a kind of prediction off-grid method and apparatus of user of the present invention, there is following beneficial technique effect：This
A kind of bright prediction off-grid method of user, proposes a kind of user behavior and excavates mode, coordinate corresponding algorithm, calculate user's
Benchmark liveness, from the trend of user activity, predicts the off-grid possibility of user in advance, facilitates operation personnel can make accordingly
Remedial measure.
Brief description
Fig. 1 is the flow chart of the embodiment of the present invention 1；
Fig. 2 is embodiment of the present invention behavioural matrix list of elements initial graph；
Fig. 3 is matrix element table figure behind behavior user operation website of the present invention；
Fig. 4 is the flow chart of the embodiment of the present invention 2；
Fig. 5 applies a kind of example prediction user's off-grid apparatus structure block diagram for the present invention.
Specific embodiment
By the technology contents of the detailed description present invention, structural features, realized purpose and effect, below in conjunction with embodiment
And coordinate accompanying drawing to be explained in detail.
It is an object of the invention to proposing a kind of user behavior to excavate mode, coordinating corresponding algorithm, calculating the base of user
Quasi- liveness, from the trend of user activity, predicts the off-grid possibility of user in advance, enables operation personnel to make corresponding benefit
Rescue measure.
Present invention uses existing Kalman filtering algorithm calculates the meansigma methodss of daily each unit, then operating limit
Algorithm Analysis user behavior trend.Meansigma methodss and analysis user's row of daily each unit can certainly be calculated with other algorithms
For trend.
Kalman filtering：One representative instance of Kalman filtering is limited from one group, comprises noise, to object position
The observation sequence (may have deviation) put predicts the coordinate of position and the speed of object.
Kalman filtering algorithm can have good noise reduction, improves the user's average active value being calculated of the present invention
Accuracy so that the present invention obtain user's trend comparison accurate.
In order to solve above technical problem, the invention provides a kind of prediction off-grid method and apparatus of user.
Embodiment 1, refers to Fig. 1, the invention discloses a kind of prediction off-grid method of user, including：
S1. distribution moments configuration site element table gives the register user logging in, the initial value of setting each element；
The matrix form site element table of the present embodiment includes laterally arranging and longitudinal row, is laterally classified as user behavior classification, uses
Family behavior classification includes：Browse, deliver, comment on, share, delete, be longitudinally classified as at least one website function sets classification, website
Function sets classification includes：Strange thing, photograph album, daily record, good friend, BLOG.
Concrete implementation mode first all operations of social network sites are set as a user behavior matrix, indulge
Nematic is classified with user behavior (browse, deliver, updating), is laterally combined into (new with one or more website same nature function collection
Fresh thing, photograph album) classification, each element represents the common factor of active user's behavior and website function, as shown in Fig. 2 initial value is 0；
As shown in figure 3, user often does an operation, the element value of respective operations is from increasing 1.
S2. record all of operation of described register user；
The present embodiment can in the form of with log recording record register user operation it is also possible to other common type note
The operation of record group side user.
S3. described register user operation is collected, calculated user's average active value；
The specific all operations to user, all carry out log recording, daily the daily record of user operation are collected to statistics
Server, statistical server is filtered to daily record daily and is counted, and finally calculates the active value of user.
S4. judge in predetermined amount of time, whether multiple different user's average active values are in downward trend, if determining
For off-network, if not, then return S1.
The present invention calculating user's average active value a kind of computational methods be：If user's average active value is Hd, wherein d
For the date, if ∑ is element value sum, if T is the valuation ∑ of all users being calculated using average algorithm, if n is use
Family operates number, then
Wherein average algorithm specifically Kalman filtering algorithm.
Because the user of each business accesses and asks operational ton each variant, therefore function group or function group set and use
The boundary element set of family behavior is set as a unit, and the element value sum within unit is ∑, is calculated using Kalman filtering
Method calculates valuation T to the ∑ of all users, and the ratio of the ∑ of active user and T value is the active index of active user, if user
The each unit average active index on the same day is A, the trend of statistics active user's A value within a period of time, needs to calculate using filtering
Method filters out the situation that user does not reach the standard grade once in a while, if tapering off shape, can determine whether as off-network purpose user and it needs to notify to run
Personnel make related measure.
Embodiment 2
As shown in figure 4, the present embodiment difference from Example 1 also includes
S31:Element value initial value in reset site element table.Can be resetted when daily morning it is also possible to
It is that other times are resetted.
One website element allocation list is set on backstage, each element can arrange horizontal classification and longitudinally classify, user
Log in foreground daily, automatically distribute an interim site element table for it, with array mode record, the initial value of each element
For 0, caching on the server, one and only one element array of each user daily, every single stepping that user is carried out, update
The value of this element, daily morning carries out to the data of the same day all users unifying to preserve, and empties caching, the service to many clusters
Device is distributed, and needs first cohersive and integrated data, is then calculated again.Set a set time, data is arranged and calculates.
As Fig. 3, as an example of the present invention, Fig. 3 be the local module element in paradise it is assumed that a is strange thing business,
B is photograph album business, and A is navigation patterns, and B is operation behavior, and that is, corresponding M21 is to browse strange thing, and M22 is that comment is fresh
Thing, M23 is to share strange thing, and M31 is Album for glancing over pictures, and M32 is uploading pictures, and M33 is modification front cover it is assumed that first day aA region
TaA value is 3 for 5, bA region TbA value for 8, bB region TbB value for 10, aB region TaB value, certain user ∑ aA be 6, ∑ aB be 1,
∑ bA is 3, and ∑ bB is 0, then user's active index of first day is
Second day aA region TaA value is 4 for 4, bA region TbA value for 6, bB region TbB value for 13, aB region TaB value, should
User ∑ aA is 6, ∑ aB is 1, ∑ bA is 3, and ∑ bB is 0, then user's active index of first day is
And so on, when the active index of this user is in continuous decrease trend, can determine whether that this user is off-network to use
Family.
The present invention can the off-grid tendency of look-ahead user, and can draw what each local module was participated in by data
User number, and some activity actual effect.Present invention research be user behavior trend, increase or delete certain
Individual local module, will not bring negative impact to real data, and local module is more many more actual row that can show user
For tending to.
Embodiment 4, refers to Fig. 5, a kind of prediction off-grid device of user, for realizing above-mentioned method, including：
List of elements allocation unit 10, user operation records unit 20, average active value computing unit 30, downward trend judge
Unit 40, element value reset unit.
List of elements allocation unit 10 is used for distribution moments configuration site element table to the register user logging in, setting each element
Initial value；
The matrix form site element table of the present embodiment includes laterally arranging and longitudinal row, is laterally classified as user behavior classification, uses
Family behavior classification includes：Browse, deliver, comment on, share, delete, be longitudinally classified as at least one website function sets classification, website
Function sets classification includes：Strange thing, photograph album, daily record, good friend, BLOG.
Concrete implementation mode first all operations of social network sites are set as a user behavior matrix, indulge
Nematic is classified with user behavior (browse, deliver, updating), is laterally combined into (new with one or more website same nature function collection
Fresh thing, photograph album) classification, each element represents the common factor of active user's behavior and website function, as shown in Fig. 2 initial value is 0；
As shown in figure 3, user often does an operation, the element value of respective operations is from increasing 1.
User operation records unit 20, is connected with list of elements allocation unit 10, for recording all of described register user
Operation；The present embodiment can in the form of with log recording record register user operation it is also possible to other common type note
The operation of record group side user.
Average active value computing unit 30, is connected with user operation records unit 20, for grasping described register user
Make to be collected, calculate user's average active value；
The specific all operations to user, all carry out log recording, daily the daily record of user operation are collected to statistics
Server, statistical server is filtered to daily record daily and is counted, and finally calculates the active value of user；
Downward trend judging unit 40, is connected with average active value computing unit 30, many in predetermined amount of time for judging
Whether individual different user's average active value is in downward trend, if being defined as off-network, if not, then returns list of elements distribution
Unit 10.
The present invention calculating user's average active value a kind of computational methods be：If user's average active value is Hd, wherein d
For the date, if ∑ is element value sum, if T is the valuation ∑ of all users being calculated using average algorithm, if n is use
Family operates number, then
Wherein average algorithm specifically Kalman filtering algorithm.
Because the user of each business accesses and asks operational ton each variant, therefore function group or function group set and use
The boundary element set of family behavior is set as a unit, and the element value sum within unit is ∑, is calculated using Kalman filtering
Method calculates valuation T to the ∑ of all users, and the ratio of the ∑ of active user and T value is the active index of active user, if user
The each unit average active index on the same day is A, the trend of statistics active user's A value within a period of time, needs to calculate using filtering
Method filters out the situation that user does not reach the standard grade once in a while, if tapering off shape, can determine whether as off-network purpose user and it needs to notify to run
Personnel make related measure.
As shown in figure 4, the element value that the present embodiment difference from Example 1 is also included in reset site element table is initial
Value.Can be to be resetted when daily morning or other times are resetted.
One website element allocation list is set on backstage, each element can arrange horizontal classification and longitudinally classify, user
Log in foreground daily, automatically distribute an interim site element table for it, with array mode record, the initial value of each element
For 0, caching on the server, one and only one element array of each user daily, every single stepping that user is carried out, update
The value of this element, daily morning carries out to the data of the same day all users unifying to preserve, and empties caching, the service to many clusters
Device is distributed, and needs first cohersive and integrated data, is then calculated again.Set a set time, data is arranged and calculates.
Preferably embodiment, also includes being provided with element value reset unit (in figure is to illustrate), for timing reset website
Element value in the list of elements, can be to carry out the operation that resets daily morning, described calculating user's average active value includes：If net
Network is the server distribution of many clusters, needs first cohersive and integrated data, is then calculated, the described user behavior that is laterally classified as divides again
Class, including：Browse, deliver, comment on, share, delete, described longitudinal direction is classified as at least one website function sets classification, including：
Strange thing, photograph album, daily record, good friend, BLOG.
Implement a kind of prediction off-grid method and apparatus of user of the present invention, there is following beneficial technique effect：
The present invention proposes a kind of user behavior and excavates mode, coordinates certain algorithm, calculates the benchmark liveness of user, from
The trend of user activity, predicts the off-grid possibility of user in advance, enables operation personnel to make corresponding remedial measure.
Above in conjunction with accompanying drawing, embodiments of the invention are described, but the invention is not limited in above-mentioned concrete
Embodiment, above-mentioned specific embodiment is only schematically, rather than restricted, those of ordinary skill in the art
Under the enlightenment of the present invention, in the case of without departing from present inventive concept and scope of the claimed protection, also can make a lot
Form, these belong within the protection of the present invention.
Claims (10)
1. a kind of prediction off-grid method of user is it is characterised in that include：
Distribution moments configuration site element table gives the register user logging in, the initial value of setting each element；
Record all of operation of described register user；
Described register user operation is collected, is calculated user's average active value；
Judge in predetermined amount of time, whether multiple different user's average active values are in downward trend, if being defined as off-network
Purpose user, if it is not, then the register user logging in given by return to step distribution moments configuration site element table, arranges the initial of each element
Value；
Wherein, described matrix form site element table includes laterally arranging and longitudinal row, and the described user behavior that is laterally classified as is classified, institute
The longitudinal direction stated is classified as at least one website function sets classification.
2. the prediction off-grid method of user as claimed in claim 1 is it is characterised in that described user's average active value calculating side
Method is：If user's average active value is Hd, wherein d is the date, if Σ is element value sum, if T is using average algorithm pair
The valuation that the Σ of all users calculates, if n is user operation number, then
3. the prediction off-grid method of user as claimed in claim 1 is it is characterised in that also include in reset site element table
The initial value of element.
4. the prediction off-grid method of user as claimed in claim 1 is it is characterised in that described calculating user's average active value
Including：Network is the server distribution of many clusters, needs first cohersive and integrated data, is then calculated again.
5. the prediction off-grid method of user as claimed in claim 1 is it is characterised in that described user behavior classification includes：
Browse, deliver, comment on, share, delete；Described website function sets classification includes：Strange thing, photograph album, daily record, good friend,
BLOG.
6. a kind of prediction off-grid device of user, for realizing the method described in claim 1 it is characterised in that including：
List of elements allocation unit, gives, for distribution moments configuration site element table, the register user logging in, and sets the first of each element value
Initial value；
User operation records unit, is connected with described list of elements allocation unit, for recording all of of described register user
Operation；
Average active value computing unit, is connected with described user operation records unit, for operating described register user
Collected, calculate user's average active value and preserve；
User behavior Trend judgement unit, is connected with described average active value computing unit, judges multiple in predetermined amount of time
Whether different user's average active values is in downward trend；
Wherein, described matrix form site element table includes laterally arranging and longitudinal row, and described is laterally classified as user behavior classification,
Described longitudinal direction is classified as at least one website function sets classification.
7. the prediction off-grid device of user as claimed in claim 6 is it is characterised in that described average active value computing unit is adopted
Calculate user's average active value H with equation belowd：Wherein d is the date, if Σ is element value sum, if T is to be calculated using meansigma methodss
The valuation that method calculates to the Σ of all users, if n is user operation number, then
8. the prediction off-grid device of user as claimed in claim 7 is it is characterised in that also include being arranged at described average work
Element value reset unit between jump value computing unit and downward trend judging unit, for the element of the site element table that resets
Initial value.
9. the prediction off-grid device of user as claimed in claim 7 is it is characterised in that described calculating user's average active value
Including：Network is the server distribution of many clusters, needs first cohersive and integrated data, is then calculated again.
10. the prediction off-grid device of user as claimed in claim 7 is it is characterised in that described user behavior classification includes：
Browse, deliver, comment on, share, delete；Described website function sets classification includes：Strange thing, photograph album, daily record, good friend,
BLOG.
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| CN102609786B true CN102609786B (en) | 2017-02-08 |
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Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104346330B (en) * | 2013-07-23 | 2018-02-02 | 阿里巴巴集团控股有限公司 | A kind of method and device of data initialization |
| CN106022856A (en) * | 2016-05-05 | 2016-10-12 | 北京京东尚科信息技术有限公司 | Data display method and device |
| CN107370614A (en) * | 2016-05-13 | 2017-11-21 | 北京京东尚科信息技术有限公司 | Network user active degree appraisal procedure and Forecasting Methodology |
| CN109086931A (en) * | 2018-08-01 | 2018-12-25 | 中国联合网络通信集团有限公司 | Predict user's off-network method and system |
| CN111340265A (en) * | 2018-12-19 | 2020-06-26 | 北京嘀嘀无限科技发展有限公司 | Off-line intervention method and device for driver, electronic equipment and computer storage medium |
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| CN1540544A (en) * | 2003-04-22 | 2004-10-27 | 联想(北京)有限公司 | Method for tracking and controlling users' online states and information |
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Effective date of registration: 20200416 Address after: 310052 room 508, floor 5, building 4, No. 699, Wangshang Road, Changhe street, Binjiang District, Hangzhou City, Zhejiang Province Patentee after: Alibaba (China) Co.,Ltd. Address before: 2, 16, 301 rooms, 510665 Yun Yun Road, Tianhe District, Guangdong, Guangzhou Patentee before: GUANGZHOU UCWEB COMPUTER TECHNOLOGY Co.,Ltd. |