CN104427118B - Method for recommending contents and mobile terminal - Google Patents
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Abstract
The embodiment of the invention discloses a method for recommending contents and a mobile terminal. The method comprises the following steps of monitoring use information of a foreground application, and setting an application sequence according to the use information of the foreground application; determining a prediction application according to the application sequence; acquiring recommendation contents related to the prediction application and outputting the recommendation contents. According to the method and the mobile terminal disclosed by the invention, the pertinence of the recommendation contents can be improved, and the experience of users is improved.
Description
Technical field
The present invention relates to electronic technology field, more particularly, to a kind of method of commending contents and mobile terminal.
Background technology
Continuous exploitation with electronics technology and perfect, the mobile terminal such as mobile phone and panel computer has had become as people
An indispensable part in life, people not only can be communicated using these mobile terminals, can also carry out file
Transmission, shooting, object for appreciation game etc..
With the development of technology, the commending contents based on mobile terminal (include: application, news, audio and video resources, life
Information etc.) gradually increase, and content also tends to diversification, due to content recommendation increase and content recommendation does not have specific aim,
Lead to not the different demands meeting user so that the inadequate hommization of content of mobile terminal recommendation, easily user is being used
Interfere during mobile terminal, reduce the intelligent of mobile terminal, have impact on the experience of user.
Content of the invention
The embodiment of the present invention provides a kind of method of commending contents and mobile terminal, can improve being directed to of content recommendation
Property, the experience of lifting user.
In order to solve above-mentioned technical problem, first aspect present invention provides a kind of method of commending contents, it may include:
Monitor the use information of foreground application, and application sequence is arranged according to the use information of described foreground application；
Prediction application is determined according to described application sequence；
Obtain the content recommendation being associated with described prediction application, and export described content recommendation.
Based in a first aspect, in the first feasible embodiment of first aspect, described monitor making of foreground application
With information, and application sequence is arranged according to the use information of described foreground application, comprising:
Monitor the use information of at least one foreground application, obtain the use of each foreground application at least one foreground application
Duration, described use information include the mark of described foreground application, described foreground application begin to use moment and described foreground
That applies exits the foreground moment；
According to the use duration of each foreground application at least one foreground application described, calculating at least one foreground described should
With in each foreground application use parameter；
According to the use parameter of each foreground application at least one foreground application described, using time sequencing to described at least
One foreground application is ranked up, and generates application sequence.
Based on the first feasible embodiment of first aspect or first aspect, feasible in the second of first aspect
In embodiment, described prediction according to the determination of described application sequence is applied, comprising:
Forerunner's sequence and the migration series matching with described forerunner's sequence are intercepted in described application sequence；
Obtain the number of times that occurs in described application sequence of forerunner's sequence and migration series respectively in described application sequence
The number of times occurring；
The number of times of the number of times being occurred according to described forerunner's sequence and the appearance of described migration series determines prediction application.
The feasible embodiment of second based on first aspect, in the third feasible embodiment of first aspect
In, the number of times of the described number of times according to the appearance of described forerunner's sequence and the appearance of described migration series determines prediction application, comprising:
Calculate the number of times of described forerunner's sequence appearance and the ratio of the number of times of described migration series appearance；
The transition probability of the application to be predicted that described ratio is associated as described forerunner's sequence and described migration series；
The application to be predicted that described transition probability is more than predetermined threshold value is defined as prediction application.
Based on the third feasible embodiment of first aspect, in the 4th kind of feasible embodiment of first aspect
In, the length of described forerunner's sequence is l, and the length of the described migration series matching with described forerunner's sequence is l+1, and described
Forerunner's sequence is identical with the front l factor in described migration series, and described application to be predicted is last of described migration series
The individual factor, wherein, described l is positive integer.
The second of the first the feasible embodiment based on first aspect or first aspect or first aspect is feasible
The third feasible embodiment of embodiment or first aspect or the 4th kind of feasible embodiment of first aspect,
In 5th kind of feasible embodiment of one side, described acquisition applies, with described prediction, the content recommendation being associated, and exports
Described content recommendation, comprising:
Obtain described prediction and apply corresponding application type；
Obtain the content recommendation being associated with described application type, and export described content recommendation.
Second aspect present invention provides a kind of mobile terminal, it may include:
Sequence setup module, for monitoring the use information of foreground application, and the use information according to described foreground application
Setting application sequence；
Application determining module, for determining prediction application according to described application sequence；
Content obtaining output module, applies, with described prediction, the content recommendation that is associated for obtaining, and pushes away described in exporting
Recommend content.
Based on second aspect, in the first feasible embodiment of second aspect, described sequence setup module includes:
Acquiring unit is monitored in application, for monitoring the use information of at least one foreground application, obtains at least one foreground
The use duration of each foreground application in application, described use information includes the mark of described foreground application, described foreground application
Begin to use moment and described foreground application exits the foreground moment；
Parameter calculation unit, for the use duration according to each foreground application at least one foreground application described, calculates
The use parameter of each foreground application at least one foreground application described；
Sequence generating unit, for the use parameter according to each foreground application at least one foreground application described, adopts
Time sequencing is ranked up at least one foreground application described, generates application sequence.
Based on the first feasible embodiment of second aspect or second aspect, feasible in the second of second aspect
In embodiment, described application determining module includes:
Sequence truncation unit, for intercepting forerunner's sequence and matching with described forerunner's sequence in described application sequence
Migration series；
Number of times acquiring unit, for obtaining number of times and the migration series that forerunner's sequence occurs in described application sequence respectively
The number of times occurring in described application sequence；
Application determining unit, the number of times for the number of times that occurred according to described forerunner's sequence and the appearance of described migration series is true
Fixed prediction application.
The feasible embodiment of second based on second aspect, in the third feasible embodiment of second aspect
In, described application determining unit includes:
Ratio calculation subelement, the number of times that the number of times and described migration series for calculating described forerunner's sequence appearance occurs
Ratio；
Probability obtains subelement, for using described ratio as treating that described forerunner's sequence and described migration series are associated
The transition probability of prediction application；
Application determination subelement, the application to be predicted for described transition probability is more than predetermined threshold value is defined as prediction should
With.
Based on the third feasible embodiment of second aspect, in the 4th kind of feasible embodiment of second aspect
In, the length of described forerunner's sequence is l, and the length of the described migration series matching with described forerunner's sequence is l+1, and described
Forerunner's sequence is identical with the front l factor in described migration series, and described application to be predicted is last of described migration series
The individual factor, wherein, described l is positive integer.
The second of the first the feasible embodiment based on second aspect or second aspect or second aspect is feasible
The third feasible embodiment of embodiment or second aspect or the 4th kind of feasible embodiment of second aspect,
In 5th kind of feasible embodiment of two aspects, described content obtaining output module includes:
Type acquiring unit, applies corresponding application type for obtaining described prediction；
Content obtaining output unit, for obtaining the content recommendation that is associated with described application type, and is pushed away described in exporting
Recommend content.
Knowable to above-mentioned, by the use information setting application sequence to foreground application, determine that prediction should according to application sequence
With.Prediction application is determined by the use information of foreground application, user can be predicted for the service condition of different mobile terminals
The application that will use, improves the specific aim of content recommendation, decreases the impact that inaccurate content recommendation causes to user, carry
Rise the intelligent of mobile terminal so that the content that mobile terminal is recommended has more the feature of standby hommization, improve the body of user
Test.
Brief description
In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
Have technology description in required use accompanying drawing be briefly described it should be apparent that, drawings in the following description be only this
Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, acceptable
Other accompanying drawings are obtained according to these accompanying drawings.
Fig. 1 is a kind of schematic flow sheet of the method for commending contents provided in an embodiment of the present invention；
Fig. 2 is the schematic flow sheet of the method for another kind commending contents provided in an embodiment of the present invention；
Fig. 3 is a kind of structural representation of mobile terminal provided in an embodiment of the present invention；
Fig. 4 is the structural representation of sequence setup module provided in an embodiment of the present invention；
Fig. 5 is the structural representation of application determining module provided in an embodiment of the present invention；
Fig. 6 is the structural representation of application determining unit provided in an embodiment of the present invention；
Fig. 7 is the structural representation of content obtaining output module provided in an embodiment of the present invention；
Fig. 8 is the structural representation of another kind mobile terminal provided in an embodiment of the present invention.
Specific embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation description is it is clear that described embodiment is only a part of embodiment of the present invention, rather than whole embodiments.It is based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of not making creative work
Embodiment, broadly falls into the scope of protection of the invention.
In embodiments of the present invention, mobile terminal monitors the use information of foreground application, by foreground application using letter
Breath determines prediction application, obtains associated content recommendation further according to prediction application and carries out output display.Wherein, described foreground should
With the application being with prediction application in mobile terminal, described foreground application can be front in the mobile terminal preset monitored time
The application that platform ran, the use information of described foreground application can include the mark of described foreground application, described foreground application
Begin to use moment and described foreground application exit foreground moment etc..
Refer to Fig. 1, for embodiments providing a kind of schematic flow sheet of the method for commending contents.As Fig. 1 institute
Show, the method comprising the steps of of the embodiment of the present invention:
S101, monitors the use information of foreground application, and arranges application sequence according to the use information of described foreground application；
Specifically, using of at least one foreground application crossed in front stage operation in the mobile terminal preset monitored time is believed
Breath, the use information according at least one foreground application described arranges application sequence.
S102, determines prediction application according to described application sequence；
Specifically, mobile terminal intercepts forerunner's sequence and the migration matching with described forerunner's sequence in application sequence
Sequence, and count the number of times that described forerunner's sequence and described migration series occur in described application sequence, described movement respectively
Number of times that terminal occurs according to described forerunner's sequence and the number of times that described migration series occur, obtain and described forerunner's sequence and institute
State the transition probability of the application to be predicted that migration series are associated, and determined according to acquired application all to be predicted described pre-
Survey application.
S103, is obtained the content recommendation being associated with described prediction application, and exports described content recommendation；
Specifically, mobile terminal, according to the prediction application determining, obtains described prediction and applies corresponding application type, preferably
, according to described application type to the associated content recommendation of server request, described acquisition for mobile terminal server sends
Described content recommendation, carries out output display to described content recommendation.
In embodiments of the present invention, by the use information setting application sequence to foreground application, true according to application sequence
Fixed prediction application.Prediction application is determined by the use information of foreground application, the service condition of different mobile terminals can be directed to,
The application that prediction user will use, improves the specific aim of content recommendation, decreases inaccurate content recommendation and user is caused
Impact, improve the intelligent of mobile terminal so that the feature of the more standby hommization of content recommended of mobile terminal, improve
The experience of user.
Refer to Fig. 2, for embodiments providing the schematic flow sheet of the method for another kind of commending contents.As Fig. 2
Shown, the method comprising the steps of of the embodiment of the present invention:
S201, monitors the use information of at least one foreground application, obtains each foreground application at least one foreground application
Use duration, described use information include the mark of described foreground application, described foreground application begin to use moment and institute
That states foreground application exits the foreground moment；
Specifically, using of at least one foreground application crossed in front stage operation in the mobile terminal preset monitored time is believed
Breath, the described foreground application according to included in described use information begin to use the exiting of moment and described foreground application before
In the platform moment, obtain the use duration of each foreground application at least one foreground application, that is, in the duration of front stage operation.For example: can
Referring to table 1:
Table 1
| Application identities | Begin to use the moment | Exit the foreground moment |
| a | 10:01 | 10:05 |
| b | 10:06 | 10:07 |
| c | 10:08 | 10:10 |
| a | 10:11 | 10:20 |
| b | 10:21 | 10:22 |
| a | 10:23 | 10:27 |
| b | 10:28 | 10:34 |
| c | 10:42 | 10:45 |
| … | … | … |
Table 1 shows the use information of the foreground application that mobile terminal is monitored, and foreground application may include that a, b, c etc.,
Mobile terminal, according to the use information of foreground application, obtains the use duration of each foreground application, such as: foreground application a exists respectively
A length of t during the use on foregrounda=4 minutes, foreground application b a length of t in the use on foregroundb=1 minute, then foreground application c was front
A length of t during the use of platformc=2 minutes, then foreground application a a length of t in the use on foregrounda=9 minutes, subsequent foreground application b
A length of t during useb=1 minute etc..Data in table 1 is only for example.
S202, according to the use duration of each foreground application at least one foreground application described, calculate described at least one
The use parameter of each foreground application in foreground application；
Specifically, mobile terminal can be with predetermined time period for δ t, according to each foreground at least one foreground application described
The use duration of application, calculates the use parameter of each foreground application at least one foreground application described, described foreground application
Represent the quantisation metric of the use duration on foreground for this foreground application using parameter, longer using duration, bigger using parameter.Example
As: with the t in table 1aAs a example, if the use duration of foreground application a is less than predetermined time period (ta< δ t), then foreground application a
Use parameter be 1, be designated as a0；If the use duration of foreground application a is more than or equal to predetermined time period (ta>=δ t), then
The use parameter of foreground application a is n, and described foreground application a is designated as { a0、a1、……、an, wherein.
Assume δ t=3 minute, understand according to table 1, then the use parameter of foreground application a is 2, is designated as a0、a1；Foreground application b
Use parameter be 1, be designated as b0；Then the use parameter of foreground application c is 1, is designated as c0；Then the use parameter of foreground application a
For 3, it is designated as a0、a1、a2；Subsequently the use parameter of foreground application b is 1, is designated as b0Deng.Wherein, when mobile terminal is in standby shape
When state, unactivated state or performed application in the foreground of mobile terminal are system service, can be according to Preset Time
The setup algorithm of length uses parameter, is designated as 0, understands, between 10:34 to 10:42, mobile terminal is in standby according to table 1
State, unactivated state or performed application in the foreground of mobile terminal are system service, foundation δ t=3 minute, then
The use of parameter is 3, is designated as 0,0,0.
S203, according to the use parameter of each foreground application at least one foreground application described, using time sequencing to institute
State at least one foreground application to be ranked up, generate application sequence；
Specifically, mobile terminal is according to the use parameter of each foreground application at least one foreground application described, using when
Between order at least one foreground application described is ranked up, generate application sequence.
With table 1 data instance, mobile terminal, according to the use parameter of foreground application, is ranked up using time sequencing, can
To generate following sequence:
a0->a1->b0->c0->a0->a1->a2->b0->a0->a1->b0->b1->0->0->0->c0.
S204, intercepts forerunner's sequence and the migration series matching with described forerunner's sequence in described application sequence；
Specifically, can preset impact length in mobile terminal is l, and described default impact length is mobile terminal to prediction
Apply the length of the forerunner's sequence being judged, by operator or manufacturer's sets itself can be gone out.Described migration series are pre- by treating
Survey application and forerunner's sequence composition, the length of described forerunner's sequence is l, then the migration sequence that described and described forerunner's sequence matches
The length of row is l+1, and described forerunner's sequence is identical with the front l factor in described migration series, and described application to be predicted is institute
State last factor of migration series, wherein, described l is positive integer.
Mobile terminal according to described default impact length, in described application sequence intercept forerunner's sequence and with described forerunner
The migration series that sequence matches.
Assume default impact length l=2, include according to forerunner's sequence that the application sequence that step s203 generates can intercept:
a0a1、a1b0、a1a2、a2b0Deng the migration series that can intercept include: a0a1b0、a0a1a2、a1b0c0、a1a2b0Deng.
S205, the number of times that acquisition forerunner's sequence occurs in described application sequence respectively and migration series are in described application sequence
The number of times occurring in row；
Specifically, mobile terminal counts to the forerunner's sequence being intercepted and migration series, obtains forerunner's sequence in institute
State the number of times that the number of times occurring in application sequence and migration series occur in described application sequence.
The forerunner's sequence being intercepted according to application sequence and step s204 of the generation of step s203 and migration series, mobile whole
End can count to forerunner's sequence and migration series respectively, can be found in table 2 and table 3:
Table 2
| Forerunner's sequence | Occurrence number |
| a0a1 | 3 |
| a1a2 | 1 |
| a1b0 | 2 |
| a2b0 | 1 |
| … | … |
Table 3
| Migration series | Occurrence number |
| a0a1a2 | 1 |
| a0a1b0 | 2 |
| a1a2b0 | 1 |
| a1b0c0 | 1 |
| … | … |
The number of times that s206, the number of times being occurred according to described forerunner's sequence and described migration series occur determines prediction application；
Specifically, mobile terminal can calculate the number of times that the number of times of described forerunner's sequence appearance and described migration series occur
Ratio, the transition probability of the application to be predicted that described ratio is associated as described forerunner's sequence and described migration series,
Described transition probability is in condition premised on described forerunner's sequence, and described application to be predicted, will be described by the probability being used
The application to be predicted that transition probability is more than predetermined threshold value is defined as prediction application.
With the data instance of table 2 and table 3, by calculating number of times and the described migration series that described forerunner's sequence occurs respectively
The ratio of the number of times occurring, it is possible to obtain table 4:
Table 4
| Forerunner's sequence | Migration series | Ratio |
| a0a1 | a0a1a2 | 0.17 |
| a0a1 | a0a1b0 | 0.67 |
| a1a2 | a1a2b0 | 1 |
| a1b0 | a1b0c0 | 0 |
| … | … | … |
The transition probability of the application to be predicted that described ratio is associated as described forerunner's sequence and described migration series,
For example: referring to table 4, forerunner's sequence is a0a1, the migration series matching with described forerunner's sequence are a0a1a2, then can obtain with
a0a1And a0a1a2Associated application to be predicted be designated a2, that is, with a0a1Premised on condition, by the application to be predicted being used
For a, by a0a1And a0a1a2The probability 0.17 drawing is as the transition probability of application a to be predicted.
Mobile terminal can be with predetermined threshold value, and the application to be predicted that described transition probability is more than predetermined threshold value is defined as predicting
Application, for example: referring to table 4 it is assumed that predetermined threshold value is 0.5, then transition probability is applied as b, acquisition more than the prediction of predetermined threshold value
Determined by described prediction application b.
S207, is obtained the content recommendation being associated with described prediction application, and exports described content recommendation；
Specifically, prediction described in acquisition for mobile terminal applies corresponding application type it is preferred that according to described application type
The content recommendation being associated to server request, the content recommendation that described acquisition for mobile terminal is associated with described application type,
And export described content recommendation.
In embodiments of the present invention, by the use information setting application sequence to foreground application, true according to application sequence
Fixed prediction application.Prediction application is determined by the use information of foreground application, the service condition of different mobile terminals can be directed to,
The application that prediction user will use, improves the specific aim of content recommendation, decreases inaccurate content recommendation and user is caused
Impact so that the feature of the more standby hommization of content recommended of mobile terminal, and mobile terminal in real time prediction is applied into
Row obtains, and can carry out output display to content recommendation, can guide before opening next application or when starting next application
User, when opening next application, browses content recommendation, it is to avoid because network condition is not good, lead to the mobile terminal to make in user
During next application with this, output content recommendation interferes to user, optimizes pushing away that content recommendation is exported
Recommend opportunity, improve the intelligent of mobile terminal, improve the experience of user.
Refer to Fig. 3, for embodiments providing a kind of structural representation of mobile terminal.As shown in figure 3, this
The described mobile terminal 1 of bright embodiment includes:
Sequence setup module 11, for monitoring the use information of foreground application, and according to described foreground application using letter
Breath setting application sequence；
In implementing, interior at least one foreground crossed in front stage operation of described sequence setup module 11 preset monitored time
The use information of application, the use information according at least one foreground application described arranges application sequence.
Specifically, please also refer to Fig. 4, for embodiments providing the structural representation of sequence setup module.As
Shown in Fig. 4, described sequence setup module 11 includes:
Acquiring unit 111 is monitored in application, for monitoring the use information of at least one foreground application, before obtaining at least one
The use duration of each foreground application in platform application, described use information includes the mark of described foreground application, described foreground application
Begin to use moment and described foreground application exit the foreground moment；
In implementing, at least crossing in the acquiring unit 111 preset monitored time is monitored in described application in front stage operation
The use information of individual foreground application, the described foreground application according to included in described use information begin to use moment and institute
That states foreground application exits the foreground moment, obtains the use duration of each foreground application at least one foreground application, that is, on foreground
The duration running.
For example: can be found in above-mentioned table 1, table 1 shows that the foreground application that acquiring unit 111 is monitored is monitored in described application
Use information, foreground application may include that a, b, c etc., and described application monitors acquiring unit 111 according to foreground application using letter
Breath, obtains the use duration of each foreground application, such as: foreground application a a length of t in the use on foreground respectivelya=4 minutes, foreground
Application b a length of t in the use on foregroundb=1 minute, then foreground application c a length of t in the use on foregroundc=2 minutes, then before
Platform application a a length of t in the use on foregrounda=9 minutes, a length of t during the use of subsequent foreground application bb=1 minute etc..In table 1
Data is only for example.
Parameter calculation unit 112, for the use duration according to each foreground application at least one foreground application described, counts
Calculate the use parameter of each foreground application at least one foreground application described；
In implementing, mobile terminal 1 can be with predetermined time period for δ t, and described parameter calculation unit 112 is according to described
The use duration of each foreground application at least one foreground application, calculates each foreground application at least one foreground application described
Using parameter, the use parameter of described foreground application represents the quantisation metric of the use duration on foreground for this foreground application, uses
Duration is longer, bigger using parameter.For example: with the t in table 1aAs a example, if the use duration of foreground application a is less than default
Between length (ta< δ t), then the use parameter of foreground application a is 1, is designated as a0；If foreground application a using duration be more than or wait
In predetermined time period (ta>=δ t), then the use parameter of foreground application a is n, and described foreground application a is designated as { a0、
a1、……、an, wherein.
Assume δ t=3 minute, understand according to above-mentioned table 1, then the use parameter of foreground application a is 2, is designated as a0、a1；Foreground
The use parameter of application b is 1, is designated as b0；Then the use parameter of foreground application c is 1, is designated as c0；Then the making of foreground application a
It is 3 with parameter, be designated as a0、a1、a2；Subsequently the use parameter of foreground application b is 1, is designated as b0Deng.Wherein, at mobile terminal 1
When holding state, unactivated state or performed application in the foreground of mobile terminal are system service, described use
Parameter calculation unit 112 can use parameter according to the setup algorithm of predetermined time period, is designated as 0, understands according to table 1,10:
Between 34 to 10:42, mobile terminal 1 is in holding state, unactivated state or performed in the foreground of mobile terminal
Apply as system service, according to δ t=3 minute, then the use of parameter is 3, is designated as 0,0,0.
Sequence generating unit 113, for the use parameter according to each foreground application at least one foreground application described, adopts
With time sequencing, at least one foreground application described is ranked up, generates application sequence；
In implementing, described sequence generating unit 113 is according to each foreground application at least one foreground application described
Using parameter, using time sequencing, at least one foreground application described is ranked up, generates application sequence.
With above-mentioned table 1 data instance, described sequence generating unit 113 according to the use parameter of foreground application, using the time
Order is ranked up, and can generate following sequence:
a0->a1->b0->c0->a0->a1->a2->b0->a0->a1->b0->b1->0->0->0->c0.
Application determining module 12, for determining prediction application according to described application sequence；
In implementing, described application determining module 12 intercept in application sequence forerunner's sequence and with described forerunner's sequence
Arrange the migration series matching, and count what described forerunner's sequence and described migration series occurred in described application sequence respectively
The number of times that number of times, the number of times that described application determining module 12 occurs according to described forerunner's sequence and described migration series occur obtains
The transition probability of the application to be predicted being associated with described forerunner's sequence and described migration series, and needed according to acquired
Prediction application determines described prediction application.
Specifically, please also refer to Fig. 5, for embodiments providing the structural representation of application determining module.As
Shown in Fig. 5, described application determining module 12 includes:
Sequence truncation unit 121, in described application sequence intercept forerunner's sequence and with described forerunner's sequence phase
The migration series joined；
In implementing, can preset impact length in mobile terminal 1 is l, and described default impact length is mobile terminal
Length to forerunner's sequence that prediction application is judged, by operator or can go out manufacturer's sets itself.Described migration series
It is made up of application to be predicted and forerunner's sequence, the length of described forerunner's sequence is l, then described and described forerunner's sequence matches
The length of migration series is l+1, and described forerunner's sequence is identical with the front l factor in described migration series, described to be predicted answers
With last factor for described migration series, wherein, described l is positive integer.
Described sequence truncation unit 121, according to described default impact length, intercepts forerunner's sequence in described application sequence
And the migration series matching with described forerunner's sequence.
Assume default impact length l=2, the forerunner's sequence that can intercept according to the application sequence that sequence generating unit 113 generates
Including: a0a1、a1b0、a1a2、a2b0Deng the migration series that can intercept include: a0a1b0、a0a1a2、a1b0c0、a1a2b0Deng.
Number of times acquiring unit 122, for obtaining number of times and the migration that forerunner's sequence occurs in described application sequence respectively
The number of times that sequence occurs in described application sequence；
In implementing, forerunner's sequence that described number of times acquiring unit 122 is intercepted to described sequence truncation unit 121 and
Migration series are counted, and the number of times that acquisition forerunner's sequence occurs in described application sequence and migration series are in described application sequence
The number of times occurring in row.
The application sequence that generates according to described sequence generating unit 113 and before described sequence truncation unit 121 intercepted
Drive sequence and migration series, described number of times acquiring unit 122 can count to forerunner's sequence and migration series respectively, generate
Above-mentioned table 2 and table 3.
Application determining unit 123, for the number of times that occurred according to described forerunner's sequence and described migration series occur secondary
Number determines prediction application；
In implementing, described application determining unit 123 can calculate the number of times that described forerunner's sequence occurs and described move
Move the ratio of the number of times that sequence occurs, using described ratio as described forerunner's sequence and described migration series be associated to be predicted
The transition probability of application, described transition probability is in condition premised on described forerunner's sequence, and described application to be predicted will be made
Probability, the application to be predicted that described transition probability is more than predetermined threshold value is defined as predicting by described application determining unit 123
Application.
With the data instance of above-mentioned table 2 and table 3, described application determining unit 123 is passed through to calculate described forerunner's sequence respectively
The ratio of the number of times that the number of times occurring and described migration series occur, it is possible to obtain above-mentioned table 4, using described ratio as before described
Drive the transition probability of the application to be predicted that sequence and described migration series are associated, for example: referring to above-mentioned table 4, forerunner's sequence is
a0a1, the migration series matching with described forerunner's sequence are a0a1a2, then can obtain and a0a1And a0a1a2Associated treating is pre-
That surveys application is designated a2, that is, with a0a1Premised on condition, by the application to be predicted being used be a, by a0a1And a0a1a2Draw
Probability 0.17 is as the transition probability of application a to be predicted.
Mobile terminal 1 can be with predetermined threshold value, and described application determining unit 123 obtains transition probability and is more than treating of predetermined threshold value
Prediction application, described application to be predicted is defined as prediction application, for example: referring to table 4 it is assumed that predetermined threshold value is 0.5, then described
Application determining unit 123 determines that the application to be predicted that transition probability is more than predetermined threshold value is b, and described application b to be predicted is determined
For prediction application.
Specifically, please also refer to Fig. 6, for embodiments providing the structural representation of application determining unit.As
Shown in Fig. 6, described application determining unit 123 includes:
Ratio calculation subelement 1231, the number of times and described migration series for calculating described forerunner's sequence appearance occurs
The ratio of number of times；
In implementing, described ratio calculation subelement 1231 pass through to calculate respectively the number of times that described forerunner's sequence occurs and
The ratio of the number of times that described migration series occur, can generate above-mentioned table 4.
Probability obtains subelement 1232, for being associated described ratio as described forerunner's sequence and described migration series
Prediction application transition probability；
In implementing, described probability obtains subelement 1232 using described ratio as described forerunner's sequence and described migration
The transition probability of the application to be predicted that sequence is associated, for example: referring to above-mentioned table 4, forerunner's sequence is a0a1, with described forerunner's sequence
Arranging the migration series matching is a0a1a2, then can obtain and a0a1And a0a1a2Associated application to be predicted be designated a2,
I.e. with a0a1Premised on condition, by the application to be predicted being used be a, by a0a1And a0a1a2The probability 0.17 drawing is pre- as treating
Survey the transition probability of application a.
Application determination subelement 1233, the application for described transition probability is more than predetermined threshold value is defined as prediction should
With；
In implementing, mobile terminal 1 can be with predetermined threshold value, and it is big that described application determination subelement 1233 obtains transition probability
In the application to be predicted of predetermined threshold value, described application to be predicted is defined as prediction application, for example: referring to table 4 it is assumed that presetting threshold
It is worth for 0.5, then described application determination subelement 1233 determines that the application to be predicted that transition probability is more than predetermined threshold value is b, by institute
State application b to be predicted and be defined as prediction application.
Content obtaining output module 13, for obtaining the content recommendation being associated with described prediction application, and exports described
Content recommendation；
In implementing, described content obtaining output module 13, according to the prediction application determining, obtains described prediction application
Corresponding application type is it is preferred that described content obtaining output module 13 is related to server request according to described application type
The content recommendation of connection, obtains the described content recommendation that server sends, carries out output display to described content recommendation.
Specifically, please also refer to Fig. 7, for embodiments providing the structural representation of content obtaining output module
Figure.As shown in fig. 7, described content obtaining output module 13 includes:
Type acquiring unit 131, applies corresponding application type for obtaining described prediction；
In implementing, described type acquiring unit 131 obtains the prediction application that described transition probability is more than predetermined threshold value
Corresponding application type.
Content obtaining output unit 132, for obtaining the content recommendation being associated with described application type, and exports described
Content recommendation；
In implementing, described content obtaining output unit 132 is associated to server request according to described application type
Content recommendation, obtain the content recommendation that is associated with described application type, and export described content recommendation.
In embodiments of the present invention, by the use information setting application sequence to foreground application, true according to application sequence
Fixed prediction application.Prediction application is determined by the use information of foreground application, the service condition of different mobile terminals can be directed to,
The application that prediction user will use, improves the specific aim of content recommendation, decreases inaccurate content recommendation and user is caused
Impact so that the feature of the more standby hommization of content recommended of mobile terminal, and mobile terminal in real time prediction is applied into
Row obtains, and can carry out output display to content recommendation, can guide before opening next application or when starting next application
User, when opening next application, browses content recommendation, it is to avoid because network condition is not good, lead to the mobile terminal to make in user
During next application with this, output content recommendation interferes to user, optimizes pushing away that content recommendation is exported
Recommend opportunity, improve the intelligent of mobile terminal, improve the experience of user.
Refer to Fig. 8, for embodiments providing the structural representation of another kind of mobile terminal.As shown in figure 8, this
The described mobile terminal of inventive embodiments may include that processor 401, memorizer 402 and communication interface 403.Memorizer 402 is used
In store program codes.Processor 401 is used for executing the program code of storage in memorizer 402.In the embodiment of the present invention, storage
Device 402 has program stored therein code, and processor 401 is used for executing this program code, operates including execution is following: monitor foreground application
Use information, and application sequence is arranged according to the use information of described foreground application；Prediction is determined according to described application sequence
Application；Obtain the content recommendation being associated with described prediction application, and export described content recommendation.Communication interface 403, for
External device communication, such as with other terminal communications.Wherein, processor 401 connects to communication according to the program code in memorizer 402
The message that mouth 403 receives are processed, and are interacted with external equipment by communication interface 403.Processor 401 can be central authorities
Processor (central processing unit, cpu), special IC (application-specific
Integrated circuit, asic) etc..Wherein, the mobile terminal in the present embodiment can include bus 404.Processor
401st, can connect and communicate by bus 404 between memorizer 402 and communication interface 403.Wherein, memorizer 402 can wrap
Include: random access memory (random access memory, ram), read only memory (read-only memory, rom),
Disk etc. has the entity of store function.Call context in the embodiment of the present invention can be buffered in ram.
In embodiments of the present invention, by the use information setting application sequence to foreground application, true according to application sequence
Fixed prediction application.Prediction application is determined by the use information of foreground application, the service condition of different mobile terminals can be directed to,
The application that prediction user will use, improves the specific aim of content recommendation, decreases inaccurate content recommendation and user is caused
Impact, improve the intelligent of mobile terminal so that the feature of the more standby hommization of content recommended of mobile terminal, improve
The experience of user.
Through the above description of the embodiments, those skilled in the art can be understood that the present invention is permissible
Realized with hardware, or firmware is realized, or combinations thereof mode is realizing.When implemented in software, can be by above-mentioned functions
It is stored in computer-readable medium or be transmitted as the one or more instructions on computer-readable medium or code.Meter
Calculation machine computer-readable recording medium includes computer-readable storage medium and communication media, and wherein communication media includes being easy to from a place to another
Any medium of individual local transmission computer program.Storage medium can be any usable medium that computer can access.With
As a example this but be not limited to: computer-readable medium can include ram, rom, eeprom, cd-rom or other optical disc storage, disk
Storage medium or other magnetic storage apparatus or can be used in carrying or store the expectation with instruction or data structure form
Program code and can be by any other medium of computer access.In addition.Any connection can be suitable become computer
Computer-readable recording medium.For example, if software is using coaxial cable, optical fiber cable, twisted-pair feeder, Digital Subscriber Line (dsl) or such as
The wireless technology of infrared ray, radio and microwave etc is transmitted from website, server or other remote sources, then coaxial electrical
The wireless technology of cable, optical fiber cable, twisted-pair feeder, dsl or such as infrared ray, wireless and microwave etc is included in affiliated medium
In fixing.As used in the present invention, disk (disk) and dish (disc) inclusion compression laser disc (cd), laser disc, laser disc, numeral are logical
With laser disc (dvd), floppy disk and Blu-ray Disc, the replicate data of the usual magnetic of which disk, and dish then with laser Lai optical duplication
Data.Above combination above should also be as including within the protection domain of computer-readable medium.
Above disclosed be only present pre-ferred embodiments, certainly the right model of the present invention can not be limited with this
Enclose, the equivalent variations therefore made according to the claims in the present invention, still belong to the scope that the present invention is covered.
Claims (10)
1. a kind of method of commending contents is it is characterised in that include:
Monitor the use information of foreground application, and application sequence is arranged according to the use information of described foreground application；
Prediction application is determined according to described application sequence；
Obtain the content recommendation being associated with described prediction application, and export described content recommendation；
Wherein, described prediction according to the determination of described application sequence is applied, comprising:
Forerunner's sequence and the migration series matching with described forerunner's sequence are intercepted in described application sequence；
Obtain forerunner's sequence number of times occurring and migration series in described application sequence respectively to occur in described application sequence
Number of times；
The number of times of the number of times being occurred according to described forerunner's sequence and the appearance of described migration series determines prediction application；
Described migration series are made up of application to be predicted and forerunner's sequence.
2. method according to claim 1 is it is characterised in that the use information of described monitoring foreground application, and according to institute
State the use information setting application sequence of foreground application, comprising:
Monitor the use information of at least one foreground application, when obtaining the use of each foreground application at least one foreground application
Long, described use information includes the mark of described foreground application, begin to use moment and the described foreground of described foreground application should
Exit the foreground moment；
According to the use duration of each foreground application at least one foreground application described, calculate at least one foreground application described
The use parameter of each foreground application；
According to the use parameter of each foreground application at least one foreground application described, using time sequencing to described at least one
Foreground application is ranked up, and generates application sequence.
3. method according to claim 1 is it is characterised in that the described number of times and described being occurred according to described forerunner's sequence
The number of times that migration series occur determines prediction application, comprising:
Calculate the number of times of described forerunner's sequence appearance and the ratio of the number of times of described migration series appearance；
The transition probability of the application to be predicted that described ratio is associated as described forerunner's sequence and described migration series；
The application to be predicted that described transition probability is more than predetermined threshold value is defined as prediction application.
4. method according to claim 3 is it is characterised in that the length of described forerunner's sequence is l, with described forerunner's sequence
The length of the described migration series matching is the front l factor phase in l+1, and described forerunner's sequence and described migration series
With described application to be predicted is last factor of described migration series, and wherein, described l is positive integer.
5. method according to claim 1 and 2 is it is characterised in that described acquisition and described prediction apply be associated to push away
Recommend content, and export described content recommendation, comprising:
Obtain described prediction and apply corresponding application type；
Obtain the content recommendation being associated with described application type, and export described content recommendation.
6. a kind of mobile terminal is it is characterised in that include:
Sequence setup module, for monitoring the use information of foreground application, and the use information setting according to described foreground application
Application sequence；
Application determining module, for determining prediction application according to described application sequence；
Content obtaining output module, for obtaining the content recommendation being associated with described prediction application, and is exported in described recommendation
Hold；
Wherein, described application determining module includes:
Sequence truncation unit, the migration match for intercepting forerunner's sequence in described application sequence and with described forerunner's sequence
Sequence；
Number of times acquiring unit, for obtaining the number of times that forerunner's sequence occurs in described application sequence and migration series respectively in institute
State the number of times occurring in application sequence；
Application determining unit, the number of times occurring for the number of times that occurred according to described forerunner's sequence and described migration series determines in advance
Survey application；
Described migration series are made up of application to be predicted and forerunner's sequence.
7. mobile terminal according to claim 6 is it is characterised in that described sequence setup module includes:
Acquiring unit is monitored in application, for monitoring the use information of at least one foreground application, obtains at least one foreground application
In each foreground application use duration, described use information includes the mark of described foreground application, the beginning of described foreground application
Exit the foreground moment using moment and described foreground application；
Parameter calculation unit, for the use duration according to each foreground application at least one foreground application described, calculates described
The use parameter of each foreground application at least one foreground application；
Sequence generating unit, for the use parameter according to each foreground application at least one foreground application described, using the time
Order is ranked up at least one foreground application described, generates application sequence.
8. mobile terminal according to claim 6 is it is characterised in that described application determining unit includes:
Ratio calculation subelement, the ratio of the number of times that the number of times and described migration series for calculating described forerunner's sequence appearance occurs
Value；
Probability obtains subelement, for using described ratio as described forerunner's sequence and described migration series be associated to be predicted
The transition probability of application；
Application determination subelement, is defined as prediction application for described transition probability is more than the to be predicted of predetermined threshold value.
9. mobile terminal according to claim 8 is it is characterised in that the length of described forerunner's sequence is l, with described forerunner
The length of the described migration series that sequence matches is the front l factor in l+1, and described forerunner's sequence and described migration series
Identical, described application to be predicted is last factor of described migration series, and wherein, described l is positive integer.
10. the mobile terminal according to claim 6 or 7 is it is characterised in that described content obtaining output module includes:
Type acquiring unit, applies corresponding application type for obtaining described prediction；
Content obtaining output unit, for obtaining the content recommendation being associated with described application type, and is exported in described recommendation
Hold.
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| CN201310404828.9A CN104427118B (en) | 2013-09-06 | 2013-09-06 | Method for recommending contents and mobile terminal |
| PCT/CN2014/085908 WO2015032334A1 (en) | 2013-09-06 | 2014-09-04 | Content recommendation method and mobile terminal |
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| CN201310404828.9A CN104427118B (en) | 2013-09-06 | 2013-09-06 | Method for recommending contents and mobile terminal |
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| CN104750798B (en) * | 2015-03-19 | 2020-09-29 | 腾讯科技（深圳）有限公司 | Recommendation method and device for application program |
| CN105528659A (en) * | 2016-01-27 | 2016-04-27 | 浙江大学 | Mobile terminal APP usage prediction method combining with time-context based on sequence mode |
| CN110020133B (en) * | 2017-11-07 | 2023-04-07 | 腾讯科技（深圳）有限公司 | Content recommendation processing method and device, computer equipment and storage medium |
| US10885912B2 (en) | 2018-11-13 | 2021-01-05 | Motorola Solutions, Inc. | Methods and systems for providing a corrected voice command |
| CN112883275B (en) * | 2021-03-17 | 2024-01-19 | 北京乐我无限科技有限责任公司 | Live broadcast room recommendation method, device, server and medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1866996A (en) * | 2005-05-09 | 2006-11-22 | 索尼爱立信移动通信日本株式会社 | Portable terminal, information recommendation method and program |
| CN102130933A (en) * | 2010-01-13 | 2011-07-20 | 中国移动通信集团公司 | Recommending method, system and equipment based on mobile Internet |
| CN103198418A (en) * | 2013-03-15 | 2013-07-10 | 北京亿赞普网络技术有限公司 | Application recommendation method and application recommendation system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN101236563A (en) * | 2008-02-01 | 2008-08-06 | 刘峰 | Intelligent personalized service website constitution method |
| JP5445339B2 (en) * | 2010-06-08 | 2014-03-19 | ソニー株式会社 | Content recommendation device and content recommendation method |
| CN102387461A (en) * | 2011-10-18 | 2012-03-21 | 北京佳信汇通科技有限公司 | Mobile data service recommendation method, apparatus and system thereof |
| CN102567511B (en) * | 2011-12-27 | 2013-10-02 | 奇智软件（北京）有限公司 | Method and device for automatically recommending application |
| CN103279384A (en) * | 2013-05-31 | 2013-09-04 | 林吓洪 | Method and device for reporting and sharing application programs for user |
-
2013
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN1866996A (en) * | 2005-05-09 | 2006-11-22 | 索尼爱立信移动通信日本株式会社 | Portable terminal, information recommendation method and program |
| CN102130933A (en) * | 2010-01-13 | 2011-07-20 | 中国移动通信集团公司 | Recommending method, system and equipment based on mobile Internet |
| CN103198418A (en) * | 2013-03-15 | 2013-07-10 | 北京亿赞普网络技术有限公司 | Application recommendation method and application recommendation system |
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