CN105260393A - Information pushing method and device and electronic equipment - Google Patents
Information pushing method and device and electronic equipment Download PDFInfo
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- CN105260393A CN105260393A CN201510586588.8A CN201510586588A CN105260393A CN 105260393 A CN105260393 A CN 105260393A CN 201510586588 A CN201510586588 A CN 201510586588A CN 105260393 A CN105260393 A CN 105260393A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/335—Filtering based on additional data, e.g. user or group profiles
- G06F16/337—Profile generation, learning or modification
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/958—Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
Abstract
The invention provides an information pushing method, an information pushing device and electronic equipment, wherein the information pushing method is applied to a server and comprises the following steps: acquiring user behavior data sent by a client; determining the weight of different application program types on the client in each preset sub-time period according to the distribution condition of the user behavior data in the preset time period and the preset sub-time periods, wherein the preset time period comprises a plurality of preset sub-time periods; determining the main application program type in each sub-time period according to the weights of the different application program types; and determining the preset sub-time period to which the current time belongs, and pushing information associated with the type of the main application program corresponding to the preset sub-time period to which the current time belongs to the client. The method can improve the pertinence of the push information and improve the push effect.
Description
Technical field
The present invention relates to data analysis technique field, particularly relate to a kind of information-pushing method, device and electronic equipment.
Background technology
Current mobile network is developed rapidly, and mobile device develops rapidly, and server by information pushing to mobile device, can present to user.In prior art, information pushing normally application program (app) by the information pushing of this app to user.But this method for pushing does not consider user's request, push and lack specific aim, push effect undesirable.
Summary of the invention
The present invention is intended to solve one of technical matters in correlation technique at least to a certain extent.
For this reason, one object of the present invention is to propose a kind of information-pushing method, and the method can improve the specific aim of pushed information, improves and pushes effect.
Another object of the present invention is to propose a kind of information push-delivery apparatus.
For achieving the above object, the information-pushing method that first aspect present invention embodiment proposes, is applied to service end, comprises: obtain the user behavior data that client sends; According to described user behavior data in preset time period and the distribution situation on the default sub-time period, determine each weight presetting different application type in client in the sub-time period, wherein, described preset time period comprises multiple default sub-time period; According to the weight of described different application type, determine each main Application Type preset in the sub-time period; And, determine the default sub-time period belonging to current time, and to the information that the main Application Type that client push is corresponding with the default sub-time period belonging to described current time is associated.
Optionally, described according to described user behavior data in preset time period and the distribution situation preset on the sub-time period, determine each weight presetting different application type in client in the sub-time period, comprise: corresponding each default sub-time period, obtain and belong to the described user behavior data presetting the sub-time period, belong to the described user behavior data presetting the sub-time period according to described, determine the described weight presetting each application program in the sub-time period; Corresponding described each application program, determines the Application Type that described application program belongs to; According to the weight of described each application program, and the Application Type that described application program belongs to, determine the described weight presetting different application type in the sub-time period.
Optionally, the described user behavior data presetting the sub-time period is belonged to described in described basis, determine the described weight presetting each application program in the sub-time period, comprise: corresponding each user, obtain the user behavior data of described user on the described each end points presetting the sub-time period, and according to the user behavior data on described each end points, determine the main users behavioral data on each end points; The described main users behavioral data corresponding to all users is added up, and determines the described each application program presetting user's use in the sub-time period, and the service condition of each application program; According to the service condition of described each application program, determine the weight of described each application program.
Optionally, described according to the user behavior data on described each end points, determine the main users behavioral data on each end points, comprising: adopt TF-IDF algorithm, often kind of user behavior data on described each end points is calculated respectively, obtains the value after the calculating of often kind of user behavior data; According to the value of the value select progressively predetermined number from big to small after calculating, and by user behavior data corresponding for the value of selection, be defined as the main users behavioral data on each end points.
Optionally, the described described main users behavioral data corresponding to all users is added up, determine the described each application program presetting user's use in the sub-time period, and the service condition of each application program, comprise: corresponding each user, on the described each end points presetting the sub-time period, from the main users behavioral data that described user is corresponding, determine each application program that described user uses, and obtain the use duration of described each application program; Corresponding described each application program, on described each end points, statistics number of users; According to the number of users determination number of users rate of growth on two end points and number of users mean value, and, according to the use duration on two end points, determine to use duration mean value, by described number of users rate of growth, number of users mean value and described use duration mean value are defined as the service condition of each application program.
Optionally, the described service condition according to described each application program, determine the weight of described each application program, comprise: corresponding each application program, number of users rate of growth corresponding to described application program respectively, number of users mean value and use duration mean value carry out dimension process, obtain three parameters after dimension; By described go after dimension three parameters to be multiplied after, be defined as the weight of described application program.
Optionally, the described weight according to described each application program, and the Application Type that described application program belongs to, determine the described weight presetting different application type in the sub-time period, comprise: the Application Type belonged to according to described application program, determine the application program that same Application Type comprises, and obtain the weight of corresponding application program; After the weight of the application program comprised by same Application Type is added, obtain the weight of described Application Type.
Optionally, corresponding described each application program, determines to comprise the Application Type that described application program belongs to: according to the attribute data of each application program, determines the Application Type that described application program belongs to.
Optionally, the information that described main Application Type is associated, comprising: the message that described main Application Type is associated; Or, belong to the application program of described main Application Type.
Optionally, the described weight according to described different application type, determine each main Application Type preset in the sub-time period, comprise: corresponding each default sub-time period, according to the weight order from big to small of described different application type, select the Application Type of predetermined number, be defined as the described main Application Type preset in the sub-time period.
The information-pushing method that first aspect present invention embodiment proposes, by obtaining user behavior data, and then determine each main Application Type preset in the sub-time period, and push corresponding information, user behavior is analyzed, push the information more meeting user behavior, improve the specific aim of pushed information, improve and push effect.
For achieving the above object, the information push-delivery apparatus that second aspect present invention embodiment proposes, is positioned at service end, comprises: acquisition module, for obtaining the user behavior data that client sends; First determination module, for according to described user behavior data in preset time period and the distribution situation preset on the sub-time period, determine each weight presetting different application type in client in the sub-time period, wherein, described preset time period comprises multiple default sub-time period; Second determination module, for the weight according to described different application type, determines each main Application Type preset in the sub-time period; And pushing module, for determining the default sub-time period belonging to current time, and to the information that the main Application Type that client push is corresponding with the default sub-time period belonging to described current time is associated.
Optionally, corresponding each default sub-time period, described first determination module comprises: first module, the described user behavior data presetting the sub-time period is belonged to for obtaining, belong to the described user behavior data presetting the sub-time period according to described, determine the described weight presetting each application program in the sub-time period; Second unit, for the described each application program of correspondence, determines the Application Type that described application program belongs to; Unit the 3rd, for the weight according to described each application program, and the Application Type that described application program belongs to, determine the described weight presetting different application type in the sub-time period.
Optionally, described first module is specifically for corresponding each user, obtain the user behavior data of described user on the described each end points presetting the sub-time period, and according to the user behavior data on described each end points, determine the main users behavioral data on each end points; The described main users behavioral data corresponding to all users is added up, and determines the described each application program presetting user's use in the sub-time period, and the service condition of each application program; According to the service condition of described each application program, determine the weight of described each application program.
Optionally, described first module is for according to the user behavior data on described each end points, determine the main users behavioral data on each end points, comprise: adopt TF-IDF algorithm, often kind of user behavior data on described each end points is calculated respectively, obtains the value after the calculating of often kind of user behavior data; According to the value of the value select progressively predetermined number from big to small after calculating, and by user behavior data corresponding for the value of selection, be defined as the main users behavioral data on each end points.
Optionally, described first module is used for the described main users behavioral data corresponding to all users and adds up, determine the described each application program presetting user's use in the sub-time period, and the service condition of each application program, comprise: corresponding each user, on the described each end points presetting the sub-time period, from the main users behavioral data that described user is corresponding, determine each application program that described user uses, and obtain the use duration of described each application program; Corresponding described each application program, on described each end points, statistics number of users; According to the number of users determination number of users rate of growth on two end points and number of users mean value, and, according to the use duration on two end points, determine to use duration mean value, by described number of users rate of growth, number of users mean value and described use duration mean value are defined as the service condition of each application program.
Optionally, described first module is used for the service condition according to described each application program, determine the weight of described each application program, comprise: corresponding each application program, number of users rate of growth corresponding to described application program respectively, number of users mean value and use duration mean value carry out dimension process, obtain three parameters after dimension; By described go after dimension three parameters to be multiplied after, be defined as the weight of described application program.
Optionally, described Unit the 3rd, specifically for the Application Type belonged to according to described application program, determines the application program that same Application Type comprises, and obtains the weight of corresponding application program; After the weight of the application program comprised by same Application Type is added, obtain the weight of described Application Type.
Optionally, described second unit specifically for: according to the attribute data of each application program, determine the Application Type that described application program belongs to.
Optionally, the information that described pushing module is associated for the described main Application Type pushed, comprising: the message that described main Application Type is associated; Or, belong to the application program of described main Application Type.
The information push-delivery apparatus that second aspect present invention embodiment proposes, by obtaining user behavior data, and then determine each main Application Type preset in the sub-time period, and push corresponding information, user behavior is analyzed, push the information more meeting user behavior, improve the specific aim of pushed information, improve and push effect.
For achieving the above object, the electronic equipment that the present invention's the 3rd embodiment proposes, comprising: housing, processor, storer, circuit board and power circuit, wherein, circuit board is placed in the interior volume that housing surrounds, and processor and storer are arranged on circuit boards; Power circuit, for powering for each circuit of electronic equipment or device; Storer is used for stores executable programs code; Processor runs the program corresponding with executable program code by reading the executable program code stored in storer, for performing following steps: obtain the user behavior data that client sends; According to described user behavior data in preset time period and the distribution situation on the default sub-time period, determine each weight presetting different application type in client in the sub-time period, wherein, described preset time period comprises multiple default sub-time period; According to the weight of described different application type, determine each main Application Type preset in the sub-time period; And, determine the default sub-time period belonging to current time, and to the information that the main Application Type that client push is corresponding with the default sub-time period belonging to described current time is associated.
Optionally, described according to described user behavior data in preset time period and the distribution situation preset on the sub-time period, determine each weight presetting different application type in client in the sub-time period, comprise: corresponding each default sub-time period, obtain and belong to the described user behavior data presetting the sub-time period, belong to the described user behavior data presetting the sub-time period according to described, determine the described weight presetting each application program in the sub-time period; Corresponding described each application program, determines the Application Type that described application program belongs to; According to the weight of described each application program, and the Application Type that described application program belongs to, determine the described weight presetting different application type in the sub-time period.
Optionally, the described user behavior data presetting the sub-time period is belonged to described in described basis, determine the described weight presetting each application program in the sub-time period, comprise: corresponding each user, obtain the user behavior data of described user on the described each end points presetting the sub-time period, and according to the user behavior data on described each end points, determine the main users behavioral data on each end points; The described main users behavioral data corresponding to all users is added up, and determines the described each application program presetting user's use in the sub-time period, and the service condition of each application program; According to the service condition of described each application program, determine the weight of described each application program.
Optionally, described according to the user behavior data on described each end points, determine the main users behavioral data on each end points, comprising: adopt TF-IDF algorithm, often kind of user behavior data on described each end points is calculated respectively, obtains the value after the calculating of often kind of user behavior data; According to the value of the value select progressively predetermined number from big to small after calculating, and by user behavior data corresponding for the value of selection, be defined as the main users behavioral data on each end points.
Optionally, the described described main users behavioral data corresponding to all users is added up, determine the described each application program presetting user's use in the sub-time period, and the service condition of each application program, comprise: corresponding each user, on the described each end points presetting the sub-time period, from the main users behavioral data that described user is corresponding, determine each application program that described user uses, and obtain the use duration of described each application program; Corresponding described each application program, on described each end points, statistics number of users; According to the number of users determination number of users rate of growth on two end points and number of users mean value, and, according to the use duration on two end points, determine to use duration mean value, by described number of users rate of growth, number of users mean value and described use duration mean value are defined as the service condition of each application program.
Optionally, the described service condition according to described each application program, determine the weight of described each application program, comprise: corresponding each application program, number of users rate of growth corresponding to described application program respectively, number of users mean value and use duration mean value carry out dimension process, obtain three parameters after dimension; By described go after dimension three parameters to be multiplied after, be defined as the weight of described application program.
Optionally, the described weight according to described each application program, and the Application Type that described application program belongs to, determine the described weight presetting different application type in the sub-time period, comprise: the Application Type belonged to according to described application program, determine the application program that same Application Type comprises, and obtain the weight of corresponding application program; After the weight of the application program comprised by same Application Type is added, obtain the weight of described Application Type.
Optionally, corresponding described each application program, determines to comprise the Application Type that described application program belongs to: according to the attribute data of each application program, determines the Application Type that described application program belongs to.
Optionally, the information that described main Application Type is associated, comprising: the message that described main Application Type is associated; Or, belong to the application program of described main Application Type.
Optionally, the described weight according to described different application type, determine each main Application Type preset in the sub-time period, comprise: corresponding each default sub-time period, according to the weight order from big to small of described different application type, select the Application Type of predetermined number, be defined as the described main Application Type preset in the sub-time period.
The electronic equipment that the present invention's the 3rd embodiment proposes, by obtaining user behavior data, and then determine each main Application Type preset in the sub-time period, and push corresponding information, user behavior is analyzed, push the information more meeting user behavior, improve the specific aim of pushed information, improve and push effect.
The aspect that the present invention adds and advantage will part provide in the following description, and part will become obvious from the following description, or be recognized by practice of the present invention.
Accompanying drawing explanation
The present invention above-mentioned and/or additional aspect and advantage will become obvious and easy understand from the following description of the accompanying drawings of embodiments, wherein:
Fig. 1 is the schematic flow sheet of the information-pushing method that one embodiment of the invention proposes;
Fig. 2 is the schematic flow sheet of the weight determining different application type in each time period in the embodiment of the present invention;
Fig. 3 is the schematic flow sheet of the weight determining each application program of each time period in the embodiment of the present invention;
Fig. 4 is the schematic flow sheet determining main users behavioral data in the embodiment of the present invention;
Fig. 5 is the schematic flow sheet of the service condition determining each application program in the embodiment of the present invention;
Fig. 6 is the schematic flow sheet of the weight determining each application program in the embodiment of the present invention according to the service condition of each application program;
Fig. 7 is the structural representation of the information push-delivery apparatus that another embodiment of the present invention proposes;
Fig. 8 is the structural representation of the information push-delivery apparatus that another embodiment of the present invention proposes.
Embodiment
Be described below in detail embodiments of the invention, the example of described embodiment is shown in the drawings, and wherein same or similar label represents same or similar module or has module that is identical or similar functions from start to finish.Being exemplary below by the embodiment be described with reference to the drawings, only for explaining the present invention, and can not limitation of the present invention being interpreted as.On the contrary, embodiments of the invention comprise fall into attached claims spirit and intension within the scope of all changes, amendment and equivalent.
Fig. 1 is the schematic flow sheet of the information-pushing method that one embodiment of the invention proposes, and the method is applied to service end, and the method comprises:
S11: obtain the user behavior data that client sends.
Wherein, user, on the mobile apparatus can data corresponding to recording user behavior after use mobile device.By monitoring user behavior, can recording user behavioral data.Thus obtain user behavior data according to recorded information.User behavior data, after getting user behavior data, can be sent to service end by client (for mobile device).
Mobile device, when recording user behavioral data, with the record start point preset, and can carry out the record of user behavior data according to the record period preset.
Such as, from 0 o'clock, according to every 1 hour record mode once, obtain each user 0,1,2 ..., the user behavior data of each time point of 23.
User behavior data comprises: user behavior and corresponding temporal information.
User behavior comprises: the title of the application program (app) that user uses, such as, and phone, micro-letter, certain Games Software etc.
Corresponding temporal information such as comprises: the start-up time of app, the post-set time of app, or, the use duration etc. of app.
Concrete, temporal information can comprise following a few kind:
The basic function time, call, browse note, use dialer, calendar, counter, alarm clock equal time;
Working time: reading mail, reading documents, and the time of some associated efficiency class app;
Degree of depth service time: use during app and be in the time of spreading its tail always;
With service time: be in during use app and spread its tail, be in the time of screen locking afterwards.
S12: according to described user behavior data in preset time period and the distribution situation on the default sub-time period, determines each weight presetting different application type in client in the sub-time period, and wherein, described preset time period comprises multiple default sub-time period.
Wherein, the default sub-time period divides in advance preset time period and obtains, concrete, starts, until the statistics end point of preset time period, according to the measurement period preset, to obtain the default sub-time period with the statistics starting point of preset time period.Statistics starting point, statistics end point and measurement period, can be identical with record period or different from above-mentioned record start point, end of record (EOR) point.
Such as, the statistics starting point of preset time period is 0 point, the statistics end point of preset time period is 24 points (0 points of next time period), and the measurement period preset is 2 hours, then divide default sub-time period of obtaining respectively: (0,2), (2,4) ... (20,22), (22,0).
Corresponding eachly preset the sub-time period, this presets in sub-time period, and the weight of the application program that the weight of an Application Type can comprise according to this Application Type is determined.
Optionally, see Fig. 2, corresponding each default sub-time period, this weight constant current journey really presetting different application type in sub-time period can comprise:
S21: obtain and belong to the described user behavior data presetting the sub-time period, belongs to the described user behavior data presetting the sub-time period according to described, determines the described weight presetting each application program in the sub-time period.
Corresponding eachly preset the sub-time period, the user behavior data in this sub-time period can be obtained respectively, then according to the weight of the user behavior data determination application program in this sub-time period.
When calculating the weight of each application program, first can analyze the main behavior of each user on two end points of this time period according to user behavior data, data corresponding according to main behavior again determine the service condition of this application program in this time period, determine weight according to service condition.
Optionally, see Fig. 3, described in described basis, belong to the described user behavior data presetting the sub-time period, determine the described weight presetting each application program in the sub-time period, comprising:
S31: corresponding each user, obtains the user behavior data of described user on the described each end points presetting the sub-time period, and according to the user behavior data on described each end points, determines the main users behavioral data on each end points.
Such as, the time period of current calculating is (6,8), then corresponding each user, obtain this user at the user behavior data of 6, and at the user behavior data of 8, further, determine that this user is at the main users behavioral data of 6, and at the main users behavioral data of 8.
Optionally, see Fig. 4, the main users behavioral data on each end points can be determined in the following way:
S41: adopt word frequency (TermFrequency, TF)-reverse text frequency (InverseDocumentFrequency, IDF) algorithm, calculates respectively often kind of user behavior data on described each end points, obtains the value after the calculating of often kind of user behavior data.
In the present embodiment, often kind of user behavior data can comprise: the title of the application program that user uses, and the use duration of each application program of user's use.Such as, according to the user behavior data of record, at 6 points, the application program that user uses comprises: micro-letter, make a phone call, and micro-courier is with 5 minutes, use 10 minutes of making a phone call.
In the present embodiment, the computing formula of TF-IDF can be:
The use duration * log of the value after the calculating of an app=this app current point in time (whole day use all app T.T./whole day uses T.T. of this app).
Be understandable that, above-mentioned computing formula is a kind of example, can adjust according to real data, such as, the time will be adjusted to frequency, or, remove log etc.
Not comprise log computing, suppose, at 6 points, micro-courier was with 5 minutes, and use 10 minutes of making a phone call, micro-letter whole day service time is 10 minutes, and whole day of making a phone call service time is 100 minutes, in addition, supposes that all app that whole day uses are exactly micro-letter and make a phone call.
Value=5* (110/10)=55 after the computing that then micro-letter is corresponding,
Value=10* (110/100)=11 after the computing of correspondence of making a phone call.
S42: according to the value of the value select progressively predetermined number from big to small after calculating, and by user behavior data corresponding for the value of selection, be defined as the main users behavioral data on each end points.
For above-mentioned example, suppose from micro-letter and middle selection one of making a phone call as main users behavior, then because the value after the computing of micro-letter correspondence is greater than the value after corresponding computing of making a phone call, then at 6 points, main users behavior is micro-letter, main users behavioral data is the data that micro-letter is corresponding, such as, also comprise micro-courier's duration etc.
Similar, the main users behavioral data of 8 can implement.
S32: the described main users behavioral data corresponding to all users is added up, determines the described each application program presetting user's use in the sub-time period, and the service condition of each application program.
See Fig. 5, optionally, the described described main users behavioral data corresponding to all users is added up, and determines the described each application program presetting user's use in the sub-time period, and the service condition of each application program, comprising:
S51: corresponding each user, on the described each end points presetting the sub-time period, determines each application program that described user uses, and obtains the use duration of described each application program from the main users behavioral data that described user is corresponding.
Such as, through above-mentioned calculating, user A comprises at the main users behavioral data of 6: use micro-letter, and uses duration, then user A is micro-letter in each application program that 6 use, and obtains the use duration of micro-letter.
S52: corresponding described each application program, on described each end points, statistics number of users.
Such as, at 6 points, all users comprise user A and user B, the main users behavior (app namely used) of user A comprises: b1, b2, b3, b4, the main users behavior (app namely used) of user B comprises: b1, b3, b5, so, at 6 points, the number of users of each app respectively: b1,2 times; B2,1 time; B3,2 times; B4,1 time; B5,1 time.
The number of users of 8 also can implement.
S53: according to the number of users determination number of users rate of growth on two end points and number of users mean value, and, according to the use duration on two end points, determine to use duration mean value, by described number of users rate of growth, number of users mean value and described use duration mean value are defined as the service condition of each application program.
Number of users rate of growth is such as with the number of users of the rear end points number of users divided by last end points.Such as, suppose a corresponding application program b1, being 2 at the numbers of users of 6, is 10 at the numbers of users of 8, then number of users rate of growth is 10/2=5.
Number of users mean value to be such as averaged computing to the number of users of two end points.Such as, suppose a corresponding application program b1, being 2 at the numbers of users of 6, is 10 at the numbers of users of 8, then number of users mean value is (2+10)/2=6.
Duration mean value is used such as to be averaged computing to the use duration of two end points.Such as, suppose a corresponding application program b1, being 5 minutes at the use durations of 6, is 10 minutes at the use durations of 8, then use duration mean value to be (5+10)/2=7.5.
S33: according to the service condition of described each application program, determines the weight of described each application program.
Wherein, after the computing of preset algorithm can being carried out to the service condition determined, corresponding weight is obtained.
Optionally, see Fig. 6, the described service condition according to described each application program, determine the weight of described each application program, comprising:
S61: corresponding each application program, number of users rate of growth corresponding to described application program respectively, number of users mean value and use duration mean value carry out dimension process, obtain three parameters after dimension.
Due to number of users rate of growth, number of users mean value and use duration mean value have different dimensions, in order to make computing have meaning, first can carry out dimension process.
Go the algorithm of dimension process not limit, such as, with currency divided by total value, the value after dimension can be obtained.For number of users rate of growth, (6,8) this period, the number of users rate of growth supposing application program b1 is 5, and (6,8) this period, the summation of the number of users rate of growth of all app is 15, then the number of users rate of growth of b1 goes to value=5/15 after dimension.
S62: by described go after dimension three parameters to be multiplied after, be defined as the weight of described application program.
Such as, in (6,8) this period, application program b1 go dimension after three parameters be A, B, C respectively, then in (6,8) this period, the weight=A*B*C of application program b1, wherein, * represents multiplication operation.
S22: corresponding described each application program, determines the Application Type that described application program belongs to.
Optionally, corresponding described each application program, determine to comprise the Application Type that described application program belongs to:
According to the attribute data of each application program, determine the Application Type that described application program belongs to.
Such as, can comprise its type belonged in the attribute data of application program, such as, micro-letter belongs to social class, and map belongs to tool-class etc.
By searching in attribute data, the Application Type that each application program belongs to can be determined.
S23: according to the weight of described each application program, and the Application Type that described application program belongs to, determine the described weight presetting different application type in the sub-time period.
Optionally, the described weight according to described each application program, and the Application Type that described application program belongs to, determine the described weight presetting different application type in the sub-time period, comprising:
According to the Application Type that described application program belongs to, determine the application program that same Application Type comprises, and obtain the weight of corresponding application program;
After the weight of the application program comprised by same Application Type is added, obtain the weight of described Application Type.
After the weight calculating each application program, the weight of all application programs under same Application Type can be added, obtain the weight of this Application Type.
Such as, this application program presetting all user's uses in the sub-time period comprises: b1, b2, b3, b4, b5, wherein, b1, b3, b4 belong to social class, and b2 belongs to tool-class, and b5 belongs to game class, b1, b2, b3, b4, the weight of b5 is w1, w2, w3, w4 respectively, w5, then the weight of social class is: w1+w3+w4, and the weight of tool-class is w2, and the weight of game class is w5.
S13: according to the weight of described different application type, determines each main Application Type preset in the sub-time period.
Optionally, the described weight according to described different application type, determine each main Application Type preset in the sub-time period, comprising:
Corresponding each default sub-time period, according to the weight order from big to small of described different application type, select the Application Type of predetermined number, be defined as the described main Application Type preset in the sub-time period.
Such as, the weight of social class is 10, and the weight of tool-class is 5, and the weight of game class is 3, supposes that predetermined number is 2, then main Application Type comprises: social class and tool-class.
S14: determine the default sub-time period belonging to current time, and to the information that the main Application Type that client push is corresponding with the default sub-time period belonging to described current time is associated.
Wherein, can default sub-time period belonging to current time determination current time, such as, current time is 7 points, then the default sub-time period belonging to is (6,8), in addition, this can also obtain the main Application Type in this default sub-time period, if the main Application Type in (6,8) this sub-time period is social class.Wherein, the quantity of the main Application Type in each sub-time period can be one or more.
Optionally, the information that main Application Type is associated, comprising:
The message that main Application Type is associated; Or,
Belong to the application program of main Application Type.
Such as, determine that main Application Type comprises: when social class and tool-class, the message of the application program about social class can be pushed, and, about the message of the application program of tool-class, or, the application program belonging to social class can be pushed, and, belong to the application program of tool-class.
In the present embodiment, by obtaining user behavior data, and then determine each main Application Type preset in the sub-time period, and push corresponding information, user behavior is analyzed, pushes the information more meeting user behavior, improve the specific aim of pushed information, improve and push effect.
Fig. 7 is the structural representation of the information push-delivery apparatus that another embodiment of the present invention proposes, and this device is positioned at service end, and this device 70 comprises:
Acquisition module 71, for obtaining the user behavior data that client sends;
Wherein, user, on the mobile apparatus can data corresponding to recording user behavior after use mobile device.By monitoring user behavior, can recording user behavioral data.Thus obtain user behavior data according to recorded information.User behavior data, after getting user behavior data, can be sent to service end by client (for mobile device).
Mobile device, when recording user behavioral data, with the record start point preset, and can carry out the record of user behavior data according to the record period preset.
Such as, from 0 o'clock, according to every 1 hour record mode once, obtain each user 0,1,2 ..., the user behavior data of each time point of 23.
User behavior data comprises: user behavior and corresponding temporal information.
User behavior comprises: the title of the application program (app) that user uses, such as, and phone, micro-letter, certain Games Software etc.
Corresponding temporal information such as comprises: the start-up time of app, the post-set time of app, or, the use duration etc. of app.
Concrete, temporal information can comprise following a few kind:
The basic function time, call, browse note, use dialer, calendar, counter, alarm clock equal time;
Working time: reading mail, reading documents, and the time of some associated efficiency class app;
Degree of depth service time: use during app and be in the time of spreading its tail always;
With service time: be in during use app and spread its tail, be in the time of screen locking afterwards.
First determination module 72, for according to described user behavior data in preset time period and the distribution situation preset on the sub-time period, determine each weight presetting different application type in client in the sub-time period, wherein, described preset time period comprises multiple default sub-time period;
Wherein, the default sub-time period divides in advance preset time period and obtains, concrete, starts, until the statistics end point of preset time period, according to the measurement period preset, to obtain the default sub-time period with the statistics starting point of preset time period.Statistics starting point, statistics end point and measurement period, can be identical with record period or different from above-mentioned record start point, end of record (EOR) point.
Such as, the statistics starting point of preset time period is 0 point, the statistics end point of preset time period is 24 points (0 points of next time period), and the measurement period preset is 2 hours, then divide default sub-time period of obtaining respectively: (0,2), (2,4) ... (20,22), (22,0).
Corresponding eachly preset the sub-time period, this presets in sub-time period, and the weight of the application program that the weight of an Application Type can comprise according to this Application Type is determined.
Optionally, see Fig. 8, corresponding each default sub-time period, described first determination module 62 comprises:
First module 721, belongs to the described user behavior data presetting the sub-time period for obtaining, and belongs to the described user behavior data presetting the sub-time period, determine the described weight presetting each application program in the sub-time period according to described;
Corresponding eachly preset the sub-time period, the user behavior data in this sub-time period can be obtained respectively, then according to the weight of the user behavior data determination application program in this sub-time period.
When calculating the weight of each application program, first can analyze the main behavior of each user on two end points of this time period according to user behavior data, data corresponding according to main behavior again determine the service condition of this application program in this time period, determine weight according to service condition.
Optionally, described first module 721 specifically for:
Corresponding each user, obtains the user behavior data of described user on the described each end points presetting the sub-time period, and according to the user behavior data on described each end points, determines the main users behavioral data on each end points;
Such as, the time period of current calculating is (6,8), then corresponding each user, obtain this user at the user behavior data of 6, and at the user behavior data of 8, further, determine that this user is at the main users behavioral data of 6, and at the main users behavioral data of 8.
The described main users behavioral data corresponding to all users is added up, and determines the described each application program presetting user's use in the sub-time period, and the service condition of each application program;
According to the service condition of described each application program, determine the weight of described each application program.
Optionally, described first module 721 for according to the user behavior data on described each end points, is determined the main users behavioral data on each end points, being comprised:
Adopt TF-IDF algorithm, often kind of user behavior data on described each end points is calculated respectively, obtains the value after the calculating of often kind of user behavior data;
In the present embodiment, often kind of user behavior data can comprise: the title of the application program that user uses, and the use duration of each application program of user's use.Such as, according to the user behavior data of record, at 6 points, the application program that user uses comprises: micro-letter, make a phone call, and micro-courier is with 5 minutes, use 10 minutes of making a phone call.
In the present embodiment, the computing formula of TF-IDF can be:
The use duration * log of the value after the calculating of an app=this app current point in time (whole day use all app T.T./whole day uses T.T. of this app).
Be understandable that, above-mentioned computing formula is a kind of example, can adjust according to real data, such as, the time will be adjusted to frequency, or, remove log etc.
Not comprise log computing, suppose, at 6 points, micro-courier was with 5 minutes, and use 10 minutes of making a phone call, micro-letter whole day service time is 10 minutes, and whole day of making a phone call service time is 100 minutes, in addition, supposes that all app that whole day uses are exactly micro-letter and make a phone call.
Value=5* (110/10)=55 after the computing that then micro-letter is corresponding,
Value=10* (110/100)=11 after the computing of correspondence of making a phone call.
According to the value of the value select progressively predetermined number from big to small after calculating, and by user behavior data corresponding for the value of selection, be defined as the main users behavioral data on each end points.
For above-mentioned example, suppose from micro-letter and middle selection one of making a phone call as main users behavior, then because the value after the computing of micro-letter correspondence is greater than the value after corresponding computing of making a phone call, then at 6 points, main users behavior is micro-letter, main users behavioral data is the data that micro-letter is corresponding, such as, also comprise micro-courier's duration etc.
Similar, the main users behavioral data of 8 can implement.
Optionally, described first module 721 is added up for the described main users behavioral data corresponding to all users, determines the described each application program presetting user's use in the sub-time period, and the service condition of each application program, comprising:
Corresponding each user, on the described each end points presetting the sub-time period, determines each application program that described user uses, and obtains the use duration of described each application program from the main users behavioral data that described user is corresponding;
Such as, through above-mentioned calculating, user A comprises at the main users behavioral data of 6: use micro-letter, and uses duration, then user A is micro-letter in each application program that 6 use, and obtains the use duration of micro-letter.
Corresponding described each application program, on described each end points, statistics number of users;
Such as, at 6 points, all users comprise user A and user B, the main users behavior (app namely used) of user A comprises: b1, b2, b3, b4, the main users behavior (app namely used) of user B comprises: b1, b3, b5, so, at 6 points, the number of users of each app respectively: b1,2 times; B2,1 time; B3,2 times; B4,1 time; B5,1 time.
The number of users of 8 also can implement.
According to the number of users determination number of users rate of growth on two end points and number of users mean value, and, according to the use duration on two end points, determine to use duration mean value, by described number of users rate of growth, number of users mean value and described use duration mean value are defined as the service condition of each application program.
Number of users rate of growth is such as with the number of users of the rear end points number of users divided by last end points.Such as, suppose a corresponding application program b1, being 2 at the numbers of users of 6, is 10 at the numbers of users of 8, then number of users rate of growth is 10/2=5.
Number of users mean value to be such as averaged computing to the number of users of two end points.Such as, suppose a corresponding application program b1, being 2 at the numbers of users of 6, is 10 at the numbers of users of 8, then number of users mean value is (2+10)/2=6.
Duration mean value is used such as to be averaged computing to the use duration of two end points.Such as, suppose a corresponding application program b1, being 5 minutes at the use durations of 6, is 10 minutes at the use durations of 8, then use duration mean value to be (5+10)/2=7.5.
Optionally, described first module 721, for the service condition according to described each application program, is determined the weight of described each application program, being comprised:
Corresponding each application program, number of users rate of growth corresponding to described application program respectively, number of users mean value and use duration mean value carry out dimension process, obtain three parameters after dimension;
Due to number of users rate of growth, number of users mean value and use duration mean value have different dimensions, in order to make computing have meaning, first can carry out dimension process.
Go the algorithm of dimension process not limit, such as, with currency divided by total value, the value after dimension can be obtained.For number of users rate of growth, (6,8) this period, the number of users rate of growth supposing application program b1 is 5, and (6,8) this period, the summation of the number of users rate of growth of all app is 15, then the number of users rate of growth of b1 goes to value=5/15 after dimension.
By described go after dimension three parameters to be multiplied after, be defined as the weight of described application program.
Such as, in (6,8) this period, application program b1 go dimension after three parameters be A, B, C respectively, then in (6,8) this period, the weight=A*B*C of application program b1, wherein, * represents multiplication operation.
Second unit 722, for the described each application program of correspondence, determines the Application Type that described application program belongs to;
Optionally, described second unit 722 specifically for:
According to the attribute data of each application program, determine the Application Type that described application program belongs to.
Such as, can comprise its type belonged in the attribute data of application program, such as, micro-letter belongs to social class, and map belongs to tool-class etc.
By searching in attribute data, the Application Type that each application program belongs to can be determined.
3rd unit 723, for the weight according to described each application program, and the Application Type that described application program belongs to, determine the described weight presetting different application type in the sub-time period.
Optionally, described 3rd unit 723 specifically for:
According to the Application Type that described application program belongs to, determine the application program that same Application Type comprises, and obtain the weight of corresponding application program;
After the weight of the application program comprised by same Application Type is added, obtain the weight of described Application Type.
After the weight calculating each application program, the weight of all application programs under same Application Type can be added, obtain the weight of this Application Type.
Such as, the application program that in this time period, all users use comprises: b1, b2, b3, b4, b5, b1, b3, b4 belongs to social class, and b2 belongs to tool-class, and b5 belongs to game class, b1, the weight of b2, b3, b4, b5 is w1 respectively, w2, w3, w4, w5, then the weight of social class is: w1+w3+w4, and the weight of tool-class is w2, and the weight of game class is w5.
Second determination module 73, for the weight according to described different application type, determines each main Application Type preset in the sub-time period;
Optionally, described second determination module 73 specifically for:
Corresponding each default sub-time period, according to the weight order from big to small of described different application type, select the Application Type of predetermined number, be defined as the described main Application Type preset in the sub-time period.
Such as, the weight of social class is 10, and the weight of tool-class is 5, and the weight of game class is 3, supposes that predetermined number is 2, then main Application Type comprises: social class and tool-class.
Pushing module 74, for determining the default sub-time period belonging to current time, and to the information that the main Application Type that client push is corresponding with the default sub-time period belonging to described current time is associated.
Optionally, the information that described pushing module 74 is associated for the described main Application Type pushed, comprising:
The message that described main Application Type is associated; Or,
Belong to the application program of described main Application Type.
Such as, determine that main Application Type comprises: when social class and tool-class, the message of the application program about social class can be pushed, and, about the message of the application program of tool-class, or, the application program belonging to social class can be pushed, and, belong to the application program of tool-class.
In the present embodiment, by obtaining user behavior data, and then determine the main Application Type in each time period, and push corresponding information, user behavior is analyzed, pushes the information more meeting user behavior, improve the specific aim of pushed information, improve and push effect.
The embodiment of the present invention also proposes a kind of electronic equipment, and this electronic equipment can specifically server.This electronic equipment comprises: housing, processor, storer, circuit board and power circuit, and wherein, circuit board is placed in the interior volume that housing surrounds, and processor and storer are arranged on circuit boards; Power circuit, for powering for each circuit of electronic equipment or device; Storer is used for stores executable programs code; Processor runs the program corresponding with executable program code by reading the executable program code stored in storer, for execution following steps:
Obtain the user behavior data that client sends;
According to described user behavior data in preset time period and the distribution situation on the default sub-time period, determine each weight presetting different application type in client in the sub-time period, wherein, described preset time period comprises multiple default sub-time period;
According to the weight of described different application type, determine each main Application Type preset in the sub-time period; And
Determine the default sub-time period belonging to current time, and to the information that the main Application Type that client push is corresponding with the default sub-time period belonging to described current time is associated.
Optionally, described according to described user behavior data in preset time period and the distribution situation preset on the sub-time period, determine each weight presetting different application type in client in the sub-time period, comprise: corresponding each default sub-time period, obtain and belong to the described user behavior data presetting the sub-time period, belong to the described user behavior data presetting the sub-time period according to described, determine the described weight presetting each application program in the sub-time period; Corresponding described each application program, determines the Application Type that described application program belongs to; According to the weight of described each application program, and the Application Type that described application program belongs to, determine the described weight presetting different application type in the sub-time period.
Optionally, the described user behavior data presetting the sub-time period is belonged to described in described basis, determine the described weight presetting each application program in the sub-time period, comprise: corresponding each user, obtain the user behavior data of described user on the described each end points presetting the sub-time period, and according to the user behavior data on described each end points, determine the main users behavioral data on each end points; The described main users behavioral data corresponding to all users is added up, and determines the described each application program presetting user's use in the sub-time period, and the service condition of each application program; According to the service condition of described each application program, determine the weight of described each application program.
Optionally, described according to the user behavior data on described each end points, determine the main users behavioral data on each end points, comprising: adopt TF-IDF algorithm, often kind of user behavior data on described each end points is calculated respectively, obtains the value after the calculating of often kind of user behavior data; According to the value of the value select progressively predetermined number from big to small after calculating, and by user behavior data corresponding for the value of selection, be defined as the main users behavioral data on each end points.
Optionally, the described described main users behavioral data corresponding to all users is added up, determine the described each application program presetting user's use in the sub-time period, and the service condition of each application program, comprise: corresponding each user, on the described each end points presetting the sub-time period, from the main users behavioral data that described user is corresponding, determine each application program that described user uses, and obtain the use duration of described each application program; Corresponding described each application program, on described each end points, statistics number of users; According to the number of users determination number of users rate of growth on two end points and number of users mean value, and, according to the use duration on two end points, determine to use duration mean value, by described number of users rate of growth, number of users mean value and described use duration mean value are defined as the service condition of each application program.
Optionally, the described service condition according to described each application program, determine the weight of described each application program, comprise: corresponding each application program, number of users rate of growth corresponding to described application program respectively, number of users mean value and use duration mean value carry out dimension process, obtain three parameters after dimension; By described go after dimension three parameters to be multiplied after, be defined as the weight of described application program.
Optionally, the described weight according to described each application program, and the Application Type that described application program belongs to, determine the described weight presetting different application type in the sub-time period, comprise: the Application Type belonged to according to described application program, determine the application program that same Application Type comprises, and obtain the weight of corresponding application program; After the weight of the application program comprised by same Application Type is added, obtain the weight of described Application Type.
Optionally, corresponding described each application program, determines to comprise the Application Type that described application program belongs to: according to the attribute data of each application program, determines the Application Type that described application program belongs to.
Optionally, the information that described main Application Type is associated, comprising: the message that described main Application Type is associated; Or, belong to the application program of described main Application Type.
Optionally, the described weight according to described different application type, determine each main Application Type preset in the sub-time period, comprise: corresponding each default sub-time period, according to the weight order from big to small of described different application type, select the Application Type of predetermined number, be defined as the described main Application Type preset in the sub-time period.
The particular content of above-mentioned steps see the associated description in above-described embodiment, can not repeat them here.
In the present embodiment, by obtaining user behavior data, and then determine each main Application Type preset in the sub-time period, and push corresponding information, user behavior is analyzed, pushes the information more meeting user behavior, improve the specific aim of pushed information, improve and push effect.
It should be noted that, in describing the invention, term " first ", " second " etc. only for describing object, and can not be interpreted as instruction or hint relative importance.In addition, in describing the invention, except as otherwise noted, the implication of " multiple " refers at least two.
Describe and can be understood in process flow diagram or in this any process otherwise described or method, represent and comprise one or more for realizing the module of the code of the executable instruction of the step of specific logical function or process, fragment or part, and the scope of the preferred embodiment of the present invention comprises other realization, wherein can not according to order that is shown or that discuss, comprise according to involved function by the mode while of basic or by contrary order, carry out n-back test, this should understand by embodiments of the invention person of ordinary skill in the field.
Should be appreciated that each several part of the present invention can realize with hardware, software, firmware or their combination.In the above-described embodiment, multiple step or method can with to store in memory and the software performed by suitable instruction execution system or firmware realize.Such as, if realized with hardware, the same in another embodiment, can realize by any one in following technology well known in the art or their combination: the discrete logic with the logic gates for realizing logic function to data-signal, there is the special IC of suitable combinational logic gate circuit, programmable gate array (PGA), field programmable gate array (FPGA) etc.
Those skilled in the art are appreciated that realizing all or part of step that above-described embodiment method carries is that the hardware that can carry out instruction relevant by program completes, described program can be stored in a kind of computer-readable recording medium, this program perform time, step comprising embodiment of the method one or a combination set of.
In addition, each functional unit in each embodiment of the present invention can be integrated in a processing module, also can be that the independent physics of unit exists, also can be integrated in a module by two or more unit.Above-mentioned integrated module both can adopt the form of hardware to realize, and the form of software function module also can be adopted to realize.If described integrated module using the form of software function module realize and as independently production marketing or use time, also can be stored in a computer read/write memory medium.
The above-mentioned storage medium mentioned can be ROM (read-only memory), disk or CD etc.
In the description of this instructions, specific features, structure, material or feature that the description of reference term " embodiment ", " some embodiments ", " example ", " concrete example " or " some examples " etc. means to describe in conjunction with this embodiment or example are contained at least one embodiment of the present invention or example.In this manual, identical embodiment or example are not necessarily referred to the schematic representation of above-mentioned term.And the specific features of description, structure, material or feature can combine in an appropriate manner in any one or more embodiment or example.
Although illustrate and describe embodiments of the invention above, be understandable that, above-described embodiment is exemplary, can not be interpreted as limitation of the present invention, and those of ordinary skill in the art can change above-described embodiment within the scope of the invention, revises, replace and modification.
Claims (10)
1. an information-pushing method, is applied to service end, it is characterized in that, comprising:
Obtain the user behavior data that client sends;
According to described user behavior data in preset time period and the distribution situation on the default sub-time period, determine each weight presetting different application type in client in the sub-time period, wherein, described preset time period comprises multiple default sub-time period;
According to the weight of described different application type, determine each main Application Type preset in the sub-time period; And
Determine the default sub-time period belonging to current time, and to the information that the main Application Type that client push is corresponding with the default sub-time period belonging to described current time is associated.
2. method according to claim 1, is characterized in that, described according to described user behavior data in preset time period and the distribution situation preset on the sub-time period, determine each weight presetting different application type in client in the sub-time period, comprising:
Corresponding each default sub-time period,
Obtain and belong to the described user behavior data presetting the sub-time period, belong to the described user behavior data presetting the sub-time period according to described, determine the described weight presetting each application program in the sub-time period;
Corresponding described each application program, determines the Application Type that described application program belongs to;
According to the weight of described each application program, and the Application Type that described application program belongs to, determine the described weight presetting different application type in the sub-time period.
3. method according to claim 2, is characterized in that, belongs to the described user behavior data presetting the sub-time period described in described basis, determines the described weight presetting each application program in the sub-time period, comprising:
Corresponding each user, obtains the user behavior data of described user on the described each end points presetting the sub-time period, and according to the user behavior data on described each end points, determines the main users behavioral data on each end points;
The described main users behavioral data corresponding to all users is added up, and determines the described each application program presetting user's use in the sub-time period, and the service condition of each application program;
According to the service condition of described each application program, determine the weight of described each application program.
4. method according to claim 3, is characterized in that, described according to the user behavior data on described each end points, determines the main users behavioral data on each end points, comprising:
Adopt TF-IDF algorithm, often kind of user behavior data on described each end points is calculated respectively, obtains the value after the calculating of often kind of user behavior data;
According to the value of the value select progressively predetermined number from big to small after calculating, and by user behavior data corresponding for the value of selection, be defined as the main users behavioral data on each end points.
5. method according to claim 3, it is characterized in that, the described described main users behavioral data corresponding to all users is added up, and determines the described each application program presetting user's use in the sub-time period, and the service condition of each application program, comprising:
Corresponding each user, on the described each end points presetting the sub-time period, determines each application program that described user uses, and obtains the use duration of described each application program from the main users behavioral data that described user is corresponding;
Corresponding described each application program, on described each end points, statistics number of users;
According to the number of users determination number of users rate of growth on two end points and number of users mean value, and, according to the use duration on two end points, determine to use duration mean value, by described number of users rate of growth, number of users mean value and described use duration mean value are defined as the service condition of each application program.
6. method according to claim 5, is characterized in that, the described service condition according to described each application program, determines the weight of described each application program, comprising:
Corresponding each application program, number of users rate of growth corresponding to described application program respectively, number of users mean value and use duration mean value carry out dimension process, obtain three parameters after dimension;
By described go after dimension three parameters to be multiplied after, be defined as the weight of described application program.
7. method according to claim 2, is characterized in that, the described weight according to described each application program, and the Application Type that described application program belongs to, and determines the described weight presetting different application type in the sub-time period, comprising:
According to the Application Type that described application program belongs to, determine the application program that same Application Type comprises, and obtain the weight of corresponding application program;
After the weight of the application program comprised by same Application Type is added, obtain the weight of described Application Type.
8. method according to claim 2, is characterized in that, corresponding described each application program, determines to comprise the Application Type that described application program belongs to:
According to the attribute data of each application program, determine the Application Type that described application program belongs to.
9. an information push-delivery apparatus, is positioned at service end, it is characterized in that, comprising:
Acquisition module, for obtaining the user behavior data that client sends;
First determination module, for according to described user behavior data in preset time period and the distribution situation preset on the sub-time period, determine each weight presetting different application type in client in the sub-time period, wherein, described preset time period comprises multiple default sub-time period;
Second determination module, for the weight according to described different application type, determines each main Application Type preset in the sub-time period; And
Pushing module, for determining the default sub-time period belonging to current time, and to the information that the main Application Type that client push is corresponding with the default sub-time period belonging to described current time is associated.
10. an electronic equipment, is characterized in that, comprising: housing, processor, storer, circuit board and power circuit, and wherein, circuit board is placed in the interior volume that housing surrounds, and processor and storer are arranged on circuit boards; Power circuit, for powering for each circuit of electronic equipment or device; Storer is used for stores executable programs code; Processor runs the program corresponding with executable program code by reading the executable program code stored in storer, for execution following steps:
Obtain the user behavior data that client sends;
According to described user behavior data in preset time period and the distribution situation on the default sub-time period, determine each weight presetting different application type in client in the sub-time period, wherein, described preset time period comprises multiple default sub-time period;
According to the weight of described different application type, determine each main Application Type preset in the sub-time period; And
Determine the default sub-time period belonging to current time, and to the information that the main Application Type that client push is corresponding with the default sub-time period belonging to described current time is associated.
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| CN111461516A (en) * | 2020-03-26 | 2020-07-28 | 深圳市百纳九洲科技有限公司 | Comprehensive grid management system platform for smart city |
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