WO2015032334A1 - 一种内容推荐的方法及移动终端 - Google Patents

一种内容推荐的方法及移动终端 Download PDF

Info

Publication number
WO2015032334A1
WO2015032334A1 PCT/CN2014/085908 CN2014085908W WO2015032334A1 WO 2015032334 A1 WO2015032334 A1 WO 2015032334A1 CN 2014085908 W CN2014085908 W CN 2014085908W WO 2015032334 A1 WO2015032334 A1 WO 2015032334A1
Authority
WO
WIPO (PCT)
Prior art keywords
application
sequence
foreground
migration
foreground application
Prior art date
Application number
PCT/CN2014/085908
Other languages
English (en)
French (fr)
Inventor
李莉
丁强
Original Assignee
华为技术有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 华为技术有限公司 filed Critical 华为技术有限公司
Publication of WO2015032334A1 publication Critical patent/WO2015032334A1/zh

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00Commerce
    • G06Q30/02Marketing; Price estimation or determination; Fundraising
    • G06Q30/0282Rating or review of business operators or products

Definitions

  • the present invention relates to the field of electronic technologies, and in particular, to a method for content recommendation and a mobile terminal.
  • the embodiment of the invention provides a method for content recommendation and a mobile terminal, which can improve the pertinence of the recommended content and enhance the user experience.
  • the first aspect of the present invention provides a method for content recommendation, which may include:
  • the recommended content associated with the predicted application is obtained and the recommended content is output.
  • the monitoring the usage information of the foreground application, and setting the application sequence according to the usage information of the foreground application includes: The usage information of the at least one foreground application is monitored, and the usage duration of each foreground application in the at least one foreground application is obtained, where the usage information includes an identifier of the foreground application, a start use time of the foreground application, and an exit foreground of the foreground application. time;
  • the determining, according to the application sequence, the prediction application includes:
  • the prediction application is determined based on the number of occurrences of the precursor sequence and the number of occurrences of the migration sequence. Based on the second possible implementation manner of the first aspect, in a third possible implementation manner of the first aspect, the determining, according to the number of occurrences of the precursor sequence and the number of occurrences of the migration sequence, determining a prediction application, including :
  • the to-be-predicted application whose migration probability is greater than a preset threshold is determined as a prediction application.
  • the length of the precursor sequence is L
  • the length of the migration sequence matching the precursor sequence L+1, and the precursor sequence and the first L factors in the migration sequence are the same
  • the to-be-predicted application is the last factor of the migration sequence, wherein the L is a positive integer.
  • a first feasible embodiment based on the first aspect or the first aspect or a second possible implementation of the first aspect or a third possible implementation of the first aspect or the fourth feasible aspect of the first aspect includes:
  • a second aspect of the present invention provides a mobile terminal, which may include:
  • a sequence setting module configured to monitor usage information of the foreground application, and set an application sequence according to the usage information of the foreground application;
  • An application determining module configured to determine a prediction application according to the application sequence
  • a content acquisition output module configured to acquire recommended content associated with the predicted application, and output the recommended content.
  • the sequence setting module includes:
  • An application monitoring acquisition unit configured to monitor usage information of the at least one foreground application, and obtain a usage duration of each foreground application in the at least one foreground application, where the usage information includes an identifier of the foreground application, a start usage time of the foreground application, and Exiting the foreground time of the foreground application;
  • a parameter calculation unit configured to calculate usage parameters of each foreground application in the at least one foreground application according to a usage duration of each foreground application in the at least one foreground application;
  • a sequence generating unit configured to sort the at least one foreground application in time sequence according to usage parameters of each foreground application in the at least one foreground application, to generate an application sequence.
  • the application determining module includes:
  • a sequence intercepting unit configured to intercept a precursor sequence and a migration sequence matching the precursor sequence in the application sequence
  • a number obtaining unit configured to separately acquire the number of occurrences of the precursor sequence in the application sequence and the number of occurrences of the migration sequence in the application sequence;
  • an application determining unit configured to determine a prediction application according to the number of occurrences of the precursor sequence and the number of occurrences of the migration sequence.
  • the application determining unit includes:
  • a ratio calculation subunit configured to calculate a ratio of a number of occurrences of the precursor sequence and a number of occurrences of the migration sequence
  • a probability acquisition subunit configured to use the ratio as a migration probability of the application to be predicted associated with the precursor sequence and the migration sequence; Forecast application.
  • the length of the precursor sequence is L
  • the length of the migration sequence matching the precursor sequence L+1, and the precursor sequence and the first L factors in the migration sequence are the same
  • the to-be-predicted application is the last factor of the migration sequence, wherein the L is a positive integer.
  • the content obtaining output module includes:
  • a type obtaining unit configured to acquire an application type corresponding to the predicted application
  • a content acquisition output unit configured to acquire recommended content associated with the application type, and output the recommended content.
  • the application sequence is set by using the usage information of the foreground application, and the prediction application is determined based on the application sequence.
  • the prediction application is determined by the usage information of the foreground application, and the application to be used by the user is predicted for different mobile terminal usage, the relevance of the recommended content is improved, the influence of the inaccurate recommended content on the user is reduced, and the mobile is improved.
  • the intelligence of the terminal makes the content recommended by the mobile terminal more humanized and enhances the user experience.
  • FIG. 1 is a schematic flowchart of a method for content recommendation according to an embodiment of the present invention
  • FIG. 2 is a schematic flowchart of another content recommendation method according to an embodiment of the present invention
  • FIG. 3 is a schematic structural diagram of a mobile terminal according to an embodiment of the present invention
  • FIG. 4 is a schematic structural diagram of a sequence setting module according to an embodiment of the present invention.
  • FIG. 5 is a schematic structural diagram of an application determining module according to an embodiment of the present invention.
  • FIG. 6 is a schematic structural diagram of an application determining unit according to an embodiment of the present invention
  • FIG. 8 is a schematic structural diagram of a content acquisition and output module according to an embodiment of the present invention
  • FIG. 8 is a schematic structural diagram of another mobile terminal according to an embodiment of the present invention. detailed description
  • the mobile terminal monitors the usage information of the foreground application, determines the prediction application by using the usage information of the foreground application, and then obtains the associated recommended content according to the prediction application for output display.
  • the foreground application and the prediction application are all applications in the mobile terminal, and the foreground application may be an application that the mobile terminal monitors in the foreground in a preset time, and the usage information of the foreground application may include the foreground application.
  • FIG. 1 is a schematic flowchart diagram of a method for content recommendation according to an embodiment of the present invention. As shown in FIG. 1, the method of the embodiment of the present invention includes the following steps:
  • Monitor usage information of the foreground application and set an application sequence according to the usage information of the foreground application.
  • the mobile terminal monitors usage information of at least one foreground application that has been run in the foreground in a preset time, and sets an application sequence according to usage information of the at least one foreground application.
  • the mobile terminal intercepts the preamble sequence and the migration sequence that matches the preamble sequence in the application sequence, and separately counts the number of occurrences of the preamble sequence and the migration sequence in the application sequence, the mobile terminal Obtaining a migration probability of the application to be predicted associated with the precursor sequence and the migration sequence according to the number of occurrences of the precursor sequence and the number of occurrences of the migration sequence, and determining the method according to all acquired applications to be predicted Forecast application.
  • the mobile terminal acquires an application type corresponding to the predicted application according to the determined predicted application, and preferably, according to the application type.
  • the recommended content is requested by the server, and the mobile terminal acquires the recommended content sent by the server, and outputs and displays the recommended content.
  • the application sequence is set by using the usage information of the foreground application, and the prediction application is determined according to the application sequence.
  • the prediction application is determined by the usage information of the foreground application, and the application to be used by the user is predicted for different mobile terminal usage, the relevance of the recommended content is improved, the influence of the inaccurate recommended content on the user is reduced, and the mobile is improved.
  • FIG. 2 a schematic flowchart of another method for content recommendation is provided according to an embodiment of the present invention. As shown in FIG. 2, the method of the embodiment of the present invention includes the following steps:
  • the usage information of the at least one foreground application is monitored, and the usage duration of each foreground application in the at least one foreground application is obtained, where the usage information includes an identifier of the foreground application, a start usage time of the foreground application, and a foreground application. Exit the front desk;
  • the mobile terminal monitors usage information of the at least one foreground application that is run in the foreground in the preset time, and obtains according to the start use time of the foreground application and the exit foreground time of the foreground application included in the usage information.
  • the duration of use of each foreground application in at least one foreground application that is, the length of time in the foreground. For example: See Table 1:
  • Table 1 shows the usage information of the foreground application that the mobile terminal monitors.
  • the foreground application may include: A, B, C, etc.
  • the data in Table 1 is only an example.
  • the mobile terminal may calculate the usage parameter of each foreground application in the at least one foreground application according to the usage duration of each foreground application in the at least one foreground application, and the usage parameter of the foreground application. Indicates the quantitative measure of the duration of use of the foreground application in the foreground. The longer the usage time, the larger the usage parameters.
  • the usage parameter of the foreground application A is 2, which is denoted as Ao
  • the usage parameter of the foreground application is 1, which is denoted as ⁇ 0
  • the usage parameter of the foreground application C is 1. Recorded as Co
  • the foreground application ⁇ uses the parameter 3, denoted as Ao, A 1 3 ⁇ 4 A 2
  • the foreground application B uses the parameter 1, which is denoted as B. Wait.
  • the usage parameter may be calculated according to the preset time length setting, and is recorded as 0.
  • S203 Sort the at least one foreground application in a time sequence according to the usage parameters of each foreground application in the at least one foreground application, to generate an application sequence.
  • the mobile terminal sorts the at least one foreground application in time sequence according to the usage parameters of each foreground application in the at least one foreground application, and generates an application sequence.
  • the mobile terminal sorts in time order according to the usage parameters of the foreground application, and can generate the following sequence:
  • the mobile terminal can preset the impact length to be L, and the preset impact length is a mobile terminal.
  • the length of the precursor sequence for judging the prediction application can be set by the operator or the manufacturer.
  • the migration sequence is composed of a to-be-predicted application and a precursor sequence, and the length of the precursor sequence is L, and the length of the migration sequence matching the precursor sequence is L+1, the precursor sequence and the migration
  • the first L factors in the sequence are the same, and the to-be-predicted application is the last factor of the migration sequence, wherein the L is a positive integer.
  • the mobile terminal intercepts the preamble sequence and the migration sequence that matches the preamble sequence in the application sequence according to the preset impact length.
  • the precursor sequence that can be intercepted according to the application sequence generated in step S203 includes: AoA AjBo , AiA 2 , ⁇ 2 ⁇ .
  • the interceptable migration sequences include: AOA BQ AJBOCQ, A ⁇ Bo, etc.
  • the mobile terminal performs statistics on the intercepted precursor sequence and the migration sequence, and obtains the number of occurrences of the precursor sequence in the application sequence and the number of times the migration sequence appears in the application sequence.
  • the mobile terminal can separately perform statistics on the precursor sequence and the migration sequence, as shown in Table 2 and Table 3:
  • the mobile terminal may calculate a ratio of the number of occurrences of the precursor sequence and the number of occurrences of the migration sequence, and use the ratio as a migration probability of the application to be predicted associated with the precursor sequence and the migration sequence.
  • the migration probability is a probability that the application to be predicted is to be used under the premise of the precursor sequence, and the application to be predicted whose migration probability is greater than a preset threshold is determined as a prediction application.
  • Table 4 can be obtained by separately calculating the ratio of the number of occurrences of the precursor sequence to the number of occurrences of the migration sequence.
  • the ratio is used as the migration probability of the to-be-predicted application associated with the precursor sequence and the migration sequence, for example: See Table 4, the precursor sequence is AoAi, and the migration sequence matching the precursor sequence is AoA Az, then The identifier of the application to be predicted associated with AoA ⁇ o AoA Az can be obtained as A 2 , that is, with AQA as a precondition, the used prediction to be applied is A, and the probability of AQA and AQA AZ is 0.17 as prediction.
  • the migration probability of application A For predictive applications, for example: See Table 4, assuming a preset threshold of 0.5, the migration probability is greater than the preset threshold
  • the prediction application is B, and the determined prediction application B is obtained.
  • S207 Obtain the recommended content that is associated with the predicted application, and output the recommended content.
  • the mobile terminal acquires an application type corresponding to the predicted application, and preferably, requests an associated server according to the application type.
  • the mobile terminal acquires recommended content associated with the application type, and outputs the recommended content.
  • the application sequence is set by using the usage information of the foreground application, and the prediction application is determined according to the application sequence.
  • the prediction application is determined by the usage information of the foreground application, and the application to be used by the user is predicted for different mobile terminal usage, the relevance of the recommended content is improved, and the influence of the inaccurate recommended content on the user is reduced, so that the mobile terminal is The recommended content is more user-friendly, and the mobile terminal obtains the predicted application in real time, and can output and display the recommended content before opening the next application or starting the next application, which can guide the user to open the next application.
  • FIG. 3 is a schematic structural diagram of a mobile terminal according to an embodiment of the present invention.
  • the mobile terminal 1 of the embodiment of the present invention includes:
  • the sequence setting module 11 is configured to monitor usage information of the foreground application, and set an application sequence according to the usage information of the foreground application;
  • the sequence setting module 11 monitors usage information of at least one foreground application that has been run in the foreground in a preset time, and sets an application sequence according to usage information of the at least one foreground application.
  • FIG. 4 is a schematic structural diagram of a sequence setting module according to an embodiment of the present invention.
  • the sequence setting module 11 includes:
  • the application monitoring acquisition unit 111 is configured to monitor the usage information of the at least one foreground application, and obtain the usage duration of each foreground application in the at least one foreground application, where the usage information includes an identifier of the foreground application, and a start time of the foreground application. And exiting the foreground time of the foreground application;
  • the application monitoring acquisition unit 111 monitors usage information of at least one foreground application that has been run in the foreground in a preset time, according to the start usage time of the foreground application and the foreground application included in the usage information. Exit the foreground time, get the usage time of each foreground application in at least one foreground application, that is, the duration of running in the foreground. For example, refer to the above table 1.
  • Table 1 shows the usage information of the foreground application monitored by the application monitoring and acquiring unit 111.
  • the foreground application may include: A, B, C, etc., and the application monitoring acquisition unit 111 according to the foreground.
  • Table 1 is only an example.
  • the parameter calculation unit 112 is configured to calculate usage parameters of each foreground application in the at least one foreground application according to the usage duration of each foreground application in the at least one foreground application.
  • the mobile terminal 1 may preset the time length as A t, and the parameter calculation unit 112 calculates the use of each foreground application in the at least one foreground application according to the usage duration of each foreground application in the at least one foreground application.
  • the parameter, the usage parameter of the foreground application indicates a quantitative measurement of the usage duration of the foreground application in the foreground, and the longer the usage duration, the larger the usage parameter.
  • the usage parameter of the foreground application A is 2, which is denoted as ⁇ , ⁇
  • the usage parameter of the foreground application is 1, which is denoted as ⁇ 0 ; then the usage parameter of the foreground application C is 1. , denoted as Co; then the foreground application ⁇ uses the parameter 3, denoted as Ao, A 1 3 ⁇ 4 A 2 ; then the foreground application B uses the parameter 1, which is recorded as Bo.
  • the sequence generating unit 113 is configured to sort the at least one foreground application in time sequence according to usage parameters of each foreground application in the at least one foreground application, to generate an application sequence;
  • the sequence generating unit 113 sorts the at least one foreground application in time sequence according to the usage parameters of each foreground application in the at least one foreground application, and generates an application. Sequence.
  • the sequence generating unit 113 sorts in time order according to the usage parameters of the foreground application, and can generate the following sequence:
  • An application determining module 12 configured to determine a prediction application according to the application sequence
  • the application determining module 12 intercepts a precursor sequence and a migration sequence that matches the precursor sequence in an application sequence, and separately counts the number of occurrences of the precursor sequence and the migration sequence in the application sequence.
  • the application determining module 12 acquires a migration probability of the application to be predicted associated with the precursor sequence and the migration sequence according to the number of occurrences of the precursor sequence and the number of occurrences of the migration sequence, and according to all acquired
  • the predictive application determines the predicted application.
  • FIG. 5 is a schematic structural diagram of an application determining module according to an embodiment of the present invention.
  • the application determining module 12 includes:
  • a sequence intercepting unit 121 configured to intercept, in the application sequence, a precursor sequence and a migration sequence that matches the precursor sequence
  • the mobile terminal 1 can preset the impact length to be L, and the preset impact length is the length of the precursor sequence for the mobile terminal to judge the predicted application, which can be set by the operator or the manufacturer.
  • the migration sequence is composed of a to-be-predicted application and a precursor sequence, and the length of the precursor sequence is L, and the length of the migration sequence matching the precursor sequence is L+1, the precursor sequence and the migration
  • the first L factors in the sequence are the same, and the to-be-predicted application is the last factor of the migration sequence, wherein the L is a positive integer.
  • the sequence intercepting unit 121 intercepts the preamble sequence and the migration sequence matching the precursor sequence in the application sequence according to the preset influence length.
  • the precursor sequence that can be intercepted according to the application sequence generated by the sequence generating unit 113 includes: AoA ⁇ AjBo, AiA 2 , A 2 B. Etc.
  • the interceptable migration sequences include: AoA Bo AoAiA 2 , AjBoCo, A ⁇ Bo, etc.
  • the number obtaining unit 122 is configured to separately acquire the number of occurrences of the precursor sequence in the application sequence and the number of times the migration sequence appears in the application sequence;
  • the number-of-times acquiring unit 122 performs statistics on the precursor sequence and the migration sequence intercepted by the sequence intercepting unit 121, and obtains the number of occurrences of the precursor sequence in the application sequence and the migration sequence appearing in the application sequence. The number of times.
  • the number obtaining unit 122 may separately perform statistics on the precursor sequence and the migration sequence to generate the above table 2 and the table. 3.
  • the application determining unit 123 is configured to determine a prediction application according to the number of occurrences of the precursor sequence and the number of occurrences of the migration sequence;
  • the application determining unit 123 may calculate a ratio of the number of occurrences of the precursor sequence and the number of occurrences of the migration sequence, and use the ratio as the to-be-predicted application associated with the precursor sequence and the migration sequence.
  • the migration probability, the migration probability is a probability that the application to be predicted is to be used under the premise of the precursor sequence, and the application determining unit 123 uses the to-be-predicted application that the migration probability is greater than a preset threshold Determined to be a forecasting application.
  • the application determining unit 123 can obtain the above table 4 by calculating the ratio of the number of occurrences of the precursor sequence and the number of occurrences of the migration sequence, respectively, and the ratio is used as
  • the migration probability of the application to be predicted associated with the precursor sequence and the migration sequence is, for example: see Table 4 above, the precursor sequence is AoAi, and the migration sequence matching the precursor sequence is ⁇ 2 , then the AoA can be obtained.
  • ⁇ o AoA Az The identifier of the application to be predicted is A 2 , which is based on the premise of ⁇ , and the to-be-predicted application to be used is A, which will be AoA ⁇ .
  • the probability of AQA AZ is 0.17 as the migration probability of application A to be predicted.
  • the mobile terminal 1 may preset a threshold, and the application determining unit 123 acquires a to-be-predicted application whose migration probability is greater than a preset threshold, and determines the to-be-predicted application as a prediction application, for example: See Table 4, assuming a preset threshold If it is 0.5, the application determining unit 123 determines that the application to be predicted whose migration probability is greater than the preset threshold is B, and determines the application B to be predicted as the prediction application.
  • FIG. 6 is a schematic structural diagram of an application determining unit according to an embodiment of the present invention.
  • the application determining unit 123 includes:
  • the ratio calculation subunit 1231 is configured to calculate a ratio of the number of occurrences of the precursor sequence and the number of occurrences of the migration sequence;
  • the ratio calculation sub-unit 1231 may generate the above table 4 by separately calculating a ratio of the number of occurrences of the precursor sequence to the number of occurrences of the migration sequence.
  • a probability acquisition sub-unit 1232 configured to use the ratio as a migration probability of a prediction application associated with the precursor sequence and the migration sequence;
  • the probability acquisition subunit 1232 uses the ratio as the precursor sequence and the location.
  • the migration probability of the application to be predicted associated with the migration sequence for example: See Table 4 above, the precursor sequence is AoAj, and the migration sequence matching the precursor sequence is AoA Az, then the correlation with ⁇ and AQA AZ can be obtained.
  • the identifier of the application to be predicted is A 2 , that is, with AQA as the precondition, the to-be-predicted application to be used is A, and the probability 0.17 derived from ⁇ AQA AZ is taken as the migration probability of the application A to be predicted.
  • An application determining subunit 1233 configured to determine, as the prediction application, the application whose migration probability is greater than a preset threshold
  • the mobile terminal 1 may preset a threshold, and the application determining sub-unit 1233 acquires a to-be-predicted application whose migration probability is greater than a preset threshold, and determines the to-be-predicted application as a prediction application, for example: Assuming that the preset threshold value is 0.5, the application determining sub-unit 1233 determines that the to-be-predicted application whose migration probability is greater than the preset threshold is B, and determines the to-be-predicted application B as the prediction application.
  • the content acquisition output module 13 is configured to acquire recommended content associated with the predicted application, and output the recommended content
  • the content acquisition and output module 13 obtains an application type corresponding to the prediction application according to the determined prediction application.
  • the content acquisition output module 13 requests the related recommended content from the server according to the application type. And obtaining the recommended content sent by the server, and outputting the recommended content.
  • FIG. 7 is a schematic structural diagram of a content acquisition and output module according to an embodiment of the present invention.
  • the content acquisition output module 13 includes:
  • the type obtaining unit 131 is configured to acquire an application type corresponding to the predicted application.
  • the type obtaining unit 131 acquires an application type corresponding to the prediction application whose migration probability is greater than a preset threshold.
  • a content obtaining output unit 132 configured to acquire recommended content associated with the application type, and output the recommended content
  • the content obtaining and outputting unit 132 requests the related recommended content from the server according to the application type, acquires recommended content associated with the application type, and outputs the recommended content.
  • the application sequence is set by using the usage information of the foreground application, and the prediction application is determined according to the application sequence.
  • the prediction application is determined by the usage information of the foreground application, and the application to be used by the user can be predicted according to the usage of different mobile terminals, and the targeted content of the recommended content is improved, and the reduction is reduced.
  • FIG. 8 is a schematic structural diagram of another mobile terminal according to an embodiment of the present invention.
  • the mobile terminal in the embodiment of the present invention may include: a processor 401, a memory 402, and a communication interface 403.
  • Memory 402 is used to store program code.
  • the processor 401 is configured to execute program code stored in the memory 402.
  • the memory 402 stores the program code
  • the processor 401 is configured to execute the program code, including: monitoring the usage information of the foreground application, and setting the application sequence according to the usage information of the foreground application; Determining the application by the application sequence; obtaining recommended content associated with the prediction application, and outputting the recommended content.
  • the communication interface 403 is configured to communicate with an external device, such as with other terminals.
  • the processor 401 processes the message received by the communication interface 403 according to the program code in the memory 402, and interacts with the external device through the communication interface 403.
  • the processor 401 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or the like.
  • the mobile terminal in this embodiment may include a bus 404.
  • the processor 401, the memory 402, and the communication interface 403 can be connected and communicated via a bus 404.
  • the memory 402 may include: a random access memory (RAM), a read-only memory (ROM), a disk and the like having a storage function.
  • RAM random access memory
  • ROM read-only memory
  • the call context in the embodiment of the present invention can be cached in the RAM.
  • the application sequence is set by using the usage information of the foreground application, and the prediction application is determined according to the application sequence.
  • the prediction application is determined by the usage information of the foreground application, and the application to be used by the user is predicted for different mobile terminal usage, the relevance of the recommended content is improved, the influence of the inaccurate recommended content on the user is reduced, and the mobile is improved.
  • the intelligence of the terminal makes the content recommended by the mobile terminal more humanized and enhances the user experience.
  • Computer readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one location to another.
  • a storage medium may be any available media that can be accessed by a computer.
  • computer readable media may comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage media or other magnetic storage device, or can be used for carrying or storing in the form of an instruction or data structure.
  • the desired program code and any other medium that can be accessed by the computer may suitably be a computer readable medium.
  • the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable , fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, wireless, and microwaves are included in the fixing of the associated media.
  • coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, wireless, and microwaves are included in the fixing of the associated media.
  • a disk and a disc include a compact disc (CD), a laser disc, a disc, a digital versatile disc (DVD), a floppy disk, and a Blu-ray disc, wherein the disc is usually magnetically copied, and the disc is The laser is used to optically replicate the data. Combinations of the above should also be included within the scope of the computer readable media.

Abstract

本发明实施例公开一种内容推荐的方法及移动终端,其中方法包括如下步骤:监听前台应用的使用信息,并根据所述前台应用的使用信息设置应用序列;根据所述应用序列确定预测应用;获取与所述预测应用相关联的推荐内容,并输出所述推荐内容。本发明可以提高推荐内容的针对性,提升用户的体验。

Description

一种内容推荐的方法及移动终端 本申请要求于 2013年 9月 6日提交中国专利局、 申请号为 201310404828.9 发明名称为"一种内容推荐的方法及移动终端"的中国专利申请的优先权,在先申 请文件的内容通过引用结合在本申请中。 技术领域 说
本发明涉及电子技术领域, 尤其涉及一种内容推荐的方法及移动终端。
背景技术
随着电子科技的不断的开发和完善, 手机和平板电脑等移动终端已经成为 书
了人们生活中不可或缺的一个部分, 人们不仅可以利用这些移动终端进行通讯, 还可以进行文件传输、 摄像、 玩游戏等。
随着技术的发展, 基于移动终端的内容推荐 (包括: 应用、 新闻、 音视频 资源、 生活资讯等)逐渐增多, 且内容也趋向多元化, 由于推荐内容的增加并 且推荐内容没有针对性, 导致无法满足用户的不同需求, 使得移动终端推荐的 内容不够人性化, 容易对用户在使用移动终端时造成干扰, 降低了移动终端的 智能性, 影响了用户的体验。
发明内容
本发明实施例提供一种内容推荐的方法及移动终端, 可以提高推荐内容的 针对性, 提升用户的体验。
为了解决上述技术问题, 本发明第一方面提供了一种内容推荐的方法, 可 包括:
监听前台应用的使用信息, 并根据所述前台应用的使用信息设置应用序列; 才艮据所述应用序列确定预测应用;
获取与所述预测应用相关联的推荐内容, 并输出所述推荐内容。
基于第一方面, 在第一方面的第一种可行的实施方式中, 所述监听前台应 用的使用信息, 并根据所述前台应用的使用信息设置应用序列, 包括: 监听至少一个前台应用的使用信息, 获取至少一个前台应用中各前台应用 的使用时长, 所述使用信息包括所述前台应用的标识、 所述前台应用的开始使 用时刻和所述前台应用的退出前台时刻;
根据所述至少一个前台应用中各前台应用的使用时长, 计算所述至少一个 前台应用中各前台应用的使用参数;
根据所述至少一个前台应用中各前台应用的使用参数, 釆用时间顺序对所 述至少一个前台应用进行排序, 生成应用序列。
基于第一方面或第一方面的第一种可行的实施方式, 在第一方面的第二种 可行的实施方式中, 所述根据所述应用序列确定预测应用, 包括:
在所述应用序列中截取前驱序列和与所述前驱序列相匹配的迁移序列; 分别获取前驱序列在所述应用序列中出现的次数和迁移序列在所述应用序 列中出现的次数;
根据所述前驱序列出现的次数和所述迁移序列出现的次数确定预测应用。 基于第一方面的第二种可行的实施方式, 在第一方面的第三种可行的实施 方式中, 所述根据所述前驱序列出现的次数和所述迁移序列出现的次数确定预 测应用, 包括:
计算所述前驱序列出现的次数和所述迁移序列出现的次数的比值; 将所述比值作为所述前驱序列和所述迁移序列相关联的待预测应用的迁移 概率;
将所述迁移概率大于预设阔值的待预测应用确定为预测应用。
基于第一方面的第三种可行的实施方式, 在第一方面的第四种可行的实施 方式中, 所述前驱序列的长度为 L, 与所述前驱序列相匹配的所述迁移序列的长 度为 L+1 , 且所述前驱序列和所述迁移序列中的前 L个因子相同, 所述待预测 应用为所述迁移序列的最后一个因子, 其中, 所述 L为正整数。
基于第一方面或第一方面的第一种可行的实施方式或第一方面的第二种可 行的实施方式或第一方面的第三种可行的实施方式或第一方面的第四种可行的 实施方式, 在第一方面的第五种可行的实施方式中, 所述获取与所述预测应用 相关联的推荐内容, 并输出所述推荐内容, 包括:
获取所述预测应用对应的应用类型;
获取与所述应用类型相关联的推荐内容, 并输出所述推荐内容。 本发明第二方面提供了一种移动终端, 可包括:
序列设置模块, 用于监听前台应用的使用信息, 并根据所述前台应用的使 用信息设置应用序列;
应用确定模块, 用于根据所述应用序列确定预测应用;
内容获取输出模块, 用于获取与所述预测应用相关联的推荐内容, 并输出 所述推荐内容。
基于第二方面, 在第二方面的第一种可行的实施方式中, 所述序列设置模 块包括:
应用监听获取单元, 用于监听至少一个前台应用的使用信息, 获取至少一 个前台应用中各前台应用的使用时长, 所述使用信息包括所述前台应用的标识、 所述前台应用的开始使用时刻和所述前台应用的退出前台时刻;
参数计算单元, 用于根据所述至少一个前台应用中各前台应用的使用时长, 计算所述至少一个前台应用中各前台应用的使用参数;
序列生成单元, 用于根据所述至少一个前台应用中各前台应用的使用参数, 釆用时间顺序对所述至少一个前台应用进行排序, 生成应用序列。
基于第二方面或第二方面的第一种可行的实施方式, 在第二方面的第二种 可行的实施方式中, 所述应用确定模块包括:
序列截取单元, 用于在所述应用序列中截取前驱序列和与所述前驱序列相 匹配的迁移序列;
次数获取单元, 用于分别获取前驱序列在所述应用序列中出现的次数和迁 移序列在所述应用序列中出现的次数;
应用确定单元, 用于根据所述前驱序列出现的次数和所述迁移序列出现的 次数确定预测应用。
基于第二方面的第二种可行的实施方式, 在第二方面的第三种可行的实施 方式中, 所述应用确定单元包括:
比值计算子单元, 用于计算所述前驱序列出现的次数和所述迁移序列出现 的次数的比值;
概率获取子单元, 用于将所述比值作为所述前驱序列和所述迁移序列相关 联的待预测应用的迁移概率; 预测应用。
基于第二方面的第三种可行的实施方式, 在第二方面的第四种可行的实施 方式中, 所述前驱序列的长度为 L, 与所述前驱序列相匹配的所述迁移序列的长 度为 L+1 , 且所述前驱序列和所述迁移序列中的前 L个因子相同, 所述待预测 应用为所述迁移序列的最后一个因子, 其中, 所述 L为正整数。
基于第二方面或第二方面的第一种可行的实施方式或第二方面的第二种可 行的实施方式或第二方面的第三种可行的实施方式或第二方面的第四种可行的 实施方式, 在第二方面的第五种可行的实施方式中, 所述内容获取输出模块包 括:
类型获取单元, 用于获取所述预测应用对应的应用类型;
内容获取输出单元, 用于获取与所述应用类型相关联的推荐内容, 并输出 所述推荐内容。
上述可知, 通过对前台应用的使用信息设置应用序列, 根据应用序列确定 预测应用。 由前台应用的使用信息确定预测应用, 可以针对不同的移动终端的 使用情况, 预测用户即将使用的应用, 提高推荐内容的针对性, 减少了不准确 的推荐内容对用户造成的影响, 提升了移动终端的智能性, 使得移动终端推荐 的内容更具备人性化的特点, 提升了用户的体验。 附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案, 下面将对实施 例或现有技术描述中所需要使用的附图作简单地介绍, 显而易见地, 下面描述 中的附图仅仅是本发明的一些实施例, 对于本领域普通技术人员来讲, 在不付 出创造性劳动的前提下, 还可以根据这些附图获得其他的附图。
图 1是本发明实施例提供的一种内容推荐的方法的流程示意图;
图 2是本发明实施例提供的另一种内容推荐的方法的流程示意图; 图 3是本发明实施例提供的一种移动终端的结构示意图;
图 4是本发明实施例提供的序列设置模块的结构示意图;
图 5是本发明实施例提供的应用确定模块的结构示意图;
图 6是本发明实施例提供的应用确定单元的结构示意图; 图 Ί是本发明实施例提供的内容获取输出模块的结构示意图; 图 8为本发明实施例提供的另一种移动终端的结构示意图。 具体实施方式
下面将结合本发明实施例中的附图, 对本发明实施例中的技术方案进行清 楚、 完整地描述, 显然, 所描述的实施例仅仅是本发明一部分实施例, 而不是 全部的实施例。 基于本发明中的实施例, 本领域普通技术人员在没有做出创造 性劳动前提下所获得的所有其他实施例, 都属于本发明保护的范围。
在本发明实施例中, 移动终端监听前台应用的使用信息, 通过前台应用的 使用信息确定预测应用, 再根据预测应用获取相关联的推荐内容进行输出显示。 其中, 所述前台应用和预测应用均为移动终端中的应用, 所述前台应用可以是 移动终端监听预设时间内在前台运行过的应用, 所述前台应用的使用信息可以 包括所述前台应用的标识、 所述前台应用的开始使用时刻和所述前台应用的退 出前台时刻等。
请参见图 1 , 为本发明实施例提供了一种内容推荐的方法的流程示意图。 如 图 1所示, 本发明实施例的所述方法包括以下步骤:
5101 , 监听前台应用的使用信息, 并根据所述前台应用的使用信息设置应 用序列;
具体的, 移动终端监听预设时间内在前台运行过的至少一个前台应用的使 用信息, 根据所述至少一个前台应用的使用信息设置应用序列。
5102, 根据所述应用序列确定预测应用;
具体的, 移动终端在应用序列中截取前驱序列以及与所述前驱序列相匹配 的迁移序列, 并分别统计所述前驱序列和所述迁移序列在所述应用序列中出现 的次数, 所述移动终端根据所述前驱序列出现的次数和所述迁移序列出现的次 数, 获取与所述前驱序列和所述迁移序列相关联的待预测应用的迁移概率, 并 根据所获取的所有待预测应用确定所述预测应用。
5103 , 获取与所述预测应用相关联的推荐内容, 并输出所述推荐内容; 具体的, 移动终端根据确定的预测应用, 获取所述预测应用对应的应用类 型, 优选的, 根据所述应用类型向服务器请求相关联的推荐内容, 所述移动终 端获取服务器发送的所述推荐内容, 对所述推荐内容进行输出显示。 在本发明实施例中, 通过对前台应用的使用信息设置应用序列, 根据应用 序列确定预测应用。 由前台应用的使用信息确定预测应用, 可以针对不同的移 动终端的使用情况, 预测用户即将使用的应用, 提高推荐内容的针对性, 减少 了不准确的推荐内容对用户造成的影响, 提升了移动终端的智能性, 使得移动 终端推荐的内容更具备人性化的特点, 提升了用户的体验。 请参见图 2, 为本发明实施例提供了另一种内容推荐的方法的流程示意图。 如图 2所示, 本发明实施例的所述方法包括以下步骤:
S201 , 监听至少一个前台应用的使用信息, 获取至少一个前台应用中各前 台应用的使用时长, 所述使用信息包括所述前台应用的标识、 所述前台应用的 开始使用时刻和所述前台应用的退出前台时刻;
具体的, 移动终端监听预设时间内在前台运行过的至少一个前台应用的使 用信息, 根据所述使用信息中所包含的所述前台应用的开始使用时刻和所述前 台应用的退出前台时刻, 获取至少一个前台应用中各前台应用的使用时长, 即 在前台运行的时长。 例如: 可参见表 1 :
表 1
Figure imgf000007_0001
表 1示出了移动终端所监听的前台应用的使用信息,前台应用可以包括: A、 B、 C等, 移动终端根据前台应用的使用信息, 分别获取每个前台应用的使用时 长, 如: 前台应用 A在前台的使用时长为 tA=4分钟, 前台应用 B在前台的使用 时长为 tB=l分钟, 然后前台应用 C在前台的使用时长为 tc=2分钟, 接着前台应 用 A在前台的使用时长为 tA=9分钟, 随后前台应用 B的使用时长为 tB=l分钟 等。 表 1中的数据仅为举例。
5202, 根据所述至少一个前台应用中各前台应用的使用时长, 计算所述至 少一个前台应用中各前台应用的使用参数;
具体的, 移动终端可以预设时间长度为 At, 根据所述至少一个前台应用中 各前台应用的使用时长, 计算所述至少一个前台应用中各前台应用的使用参数, 所述前台应用的使用参数表示该前台应用在前台的使用时长的量化度量, 使用 时长越长, 使用参数越大。 例如: 以表 1中的 为例, 如果前台应用 A的使用 时长小于预设时间长度(tA<At ), 则前台应用 A的使用参数为 1 , 记为 Ao; 如 果前台应用 A的使用时长大于或等于预设时间长度(tA>=At ), 则前台应用 A 的使用参数为 n, 所述前台应用 A被记为 {Ao、 Al An} , 其中 " =「^ /Δ 。
假设 At=3分钟, 依据表 1可知, 则前台应用 A的使用参数为 2, 记为 Ao、 Αΰ 前台应用 Β的使用参数为 1 , 记为 Β0; 然后前台应用 C的使用参数为 1 , 记为 Co; 接着前台应用 Α的使用参数为 3 , 记为 Ao、 A1 ¾ A2; 随后前台应用 B 的使用参数为 1 , 记为 B。等。 其中, 当移动终端处于待机状态、 未激活状态或 者在移动终端的前台中所执行的应用为系统服务时, 可以依据预设时间长度的 设定计算使用参数, 记为 0, 依据表 1可知, 在 10:34至 10:42之间, 移动终端 处于待机状态、 未激活状态或者在移动终端的前台中所执行的应用为系统服务, 依据 At=3分钟, 则使用参数为 3 , 记为 0、 0、 0。
5203 , 根据所述至少一个前台应用中各前台应用的使用参数, 釆用时间顺 序对所述至少一个前台应用进行排序, 生成应用序列;
具体的, 移动终端根据所述至少一个前台应用中各前台应用的使用参数, 釆用时间顺序对所述至少一个前台应用进行排序, 生成应用序列。
以表 1 数据为例, 移动终端根据前台应用的使用参数, 釆用时间顺序进行 排序, 可以生成以下序列:
Ao->Ai ->B0->C0->Ao->Ai ->A2->B0->Ao->Ai ->B0->B i ->0 ->0->0 ->C0
5204 , 在所述应用序列中截取前驱序列和与所述前驱序列相匹配的迁移序 列;
具体的,移动终端中可以预设影响长度为 L,所述预设影响长度为移动终端 对预测应用进行判断的前驱序列的长度, 可以由运营商或出厂商自行设定。 所 述迁移序列由待预测应用和前驱序列组成,所述前驱序列的长度为 L,则所述与 所述前驱序列相匹配的迁移序列的长度为 L+1 , 所述前驱序列和所述迁移序列 中的前 L个因子相同, 所述待预测应用为所述迁移序列的最后一个因子, 其中, 所述 L为正整数。
移动终端根据所述预设影响长度, 在所述应用序列中截取前驱序列和与所 述前驱序列相匹配的迁移序列。
假设预设影响长度 L=2 , 依据步骤 S203生成的应用序列可截取的前驱序列 包括: AoA AjBo , AiA2、 Α2Β。等, 可截取的迁移序列包括: AOA BQ
Figure imgf000009_0001
. AJBOCQ , A^Bo等。
S205 , 分别获取前驱序列在所述应用序列中出现的次数和迁移序列在所述 应用序列中出现的次数;
具体的, 移动终端对所截取的前驱序列和迁移序列进行统计, 获取前驱序 列在所述应用序列中出现的次数和迁移序列在所述应用序列中出现的次数。
依据步骤 S203生成的应用序列和步骤 S204所截取的前驱序列和迁移序列 , 移动终端可以分别对前驱序列和迁移序列进行统计, 可参见表 2和表 3 :
表 2
Figure imgf000009_0002
表 3
迁移序列 出现次数
ΑοΑιΑ2 1
AQAJBO 2
Figure imgf000010_0001
S206 , 根据所述前驱序列出现的次数和所述迁移序列出现的次数确定预测 应用;
具体的, 移动终端可以计算所述前驱序列出现的次数和所述迁移序列出现 的次数的比值, 将所述比值作为所述前驱序列和所述迁移序列相关联的待预测 应用的迁移概率, 所述迁移概率为在以所述前驱序列为前提条件, 所述待预测 应用将被使用的概率, 将所述迁移概率大于预设阔值的待预测应用确定为预测 应用。
以表 2和表 3的数据为例, 通过分别计算所述前驱序列出现的次数和所述 迁移序列出现的次数的比值, 可以获得表 4:
表 4
Figure imgf000010_0002
将所述比值作为所述前驱序列和所述迁移序列相关联的待预测应用的迁移 概率, 例如: 参见表 4 , 前驱序列为 AoAi , 与所述前驱序列相匹配的迁移序列 为 AoA Az, 则可以获取与 AoA^o AoA Az相关联的待预测应用的标识为 A2, 即 以 AQA为前提条件, 将被使用的待预测应用为 A, 将 AQA和 AQA AZ得出的概 率 0.17作为待预测应用 A的迁移概率。 为预测应用, 例如: 参见表 4, 假设预设阔值为 0.5 , 则迁移概率大于预设阔值 的预测应用为 B, 获取所确定的所述预测应用 B。
S207, 获取与所述预测应用相关联的推荐内容, 并输出所述推荐内容; 具体的, 移动终端获取所述预测应用对应的应用类型, 优选的, 根据所述 应用类型向服务器请求相关联的推荐内容, 所述移动终端获取与所述应用类型 相关联的推荐内容, 并输出所述推荐内容。
在本发明实施例中, 通过对前台应用的使用信息设置应用序列, 根据应用 序列确定预测应用。 由前台应用的使用信息确定预测应用, 可以针对不同的移 动终端的使用情况, 预测用户即将使用的应用, 提高推荐内容的针对性, 减少 了不准确的推荐内容对用户造成的影响, 使得移动终端推荐的内容更具备人性 化的特点, 并且移动终端实时对预测应用进行获取, 可以在打开下一应用前或 者启动下一应用时, 对推荐内容进行输出显示, 可以引导用户在打开下一应用 时, 浏览推荐内容, 避免了由于网络状况不佳, 导致移动终端在用户使用该下 一应用的过程中, 输出推荐内容对用户造成干扰, 优化了对推荐内容进行输出 的推荐时机, 提升了移动终端的智能性, 提升了用户的体验。 请参见图 3 , 为本发明实施例提供了一种移动终端的结构示意图。 如图 3所 示, 本发明实施例的所述移动终端 1包括:
序列设置模块 11 , 用于监听前台应用的使用信息, 并根据所述前台应用的 使用信息设置应用序列;
具体实现中, 所述序列设置模块 11监听预设时间内在前台运行过的至少一 个前台应用的使用信息, 根据所述至少一个前台应用的使用信息设置应用序列。
具体的,请一并参见图 4, 为本发明实施例提供了序列设置模块的结构示意 图。 如图 4所示, 所述序列设置模块 11包括:
应用监听获取单元 111 , 用于监听至少一个前台应用的使用信息, 获取至少 一个前台应用中各前台应用的使用时长, 所述使用信息包括所述前台应用的标 识、 所述前台应用的开始使用时刻和所述前台应用的退出前台时刻;
具体实现中, 所述应用监听获取单元 111 监听预设时间内在前台运行过的 至少一个前台应用的使用信息, 根据所述使用信息中所包含的所述前台应用的 开始使用时刻和所述前台应用的退出前台时刻, 获取至少一个前台应用中各前 台应用的使用时长, 即在前台运行的时长。 例如: 可参见上述表 1 , 表 1示出了所述应用监听获取单元 111所监听的前 台应用的使用信息, 前台应用可以包括: A、 B、 C等, 所述应用监听获取单元 111根据前台应用的使用信息, 分别获取每个前台应用的使用时长, 如: 前台应 用 A在前台的使用时长为 tA=4分钟, 前台应用 B在前台的使用时长为 tB=l分 钟, 然后前台应用 C在前台的使用时长为 tc=2分钟, 接着前台应用 A在前台的 使用时长为 tA=9分钟, 随后前台应用 B的使用时长为 tB=l分钟等。表 1中的数 据仅为举例。
参数计算单元 112,用于根据所述至少一个前台应用中各前台应用的使用时 长, 计算所述至少一个前台应用中各前台应用的使用参数;
具体实现中, 移动终端 1 可以预设时间长度为 A t, 所述参数计算单元 112 根据所述至少一个前台应用中各前台应用的使用时长, 计算所述至少一个前台 应用中各前台应用的使用参数, 所述前台应用的使用参数表示该前台应用在前 台的使用时长的量化度量, 使用时长越长, 使用参数越大。 例如: 以表 1 中的 为例, 如果前台应用 A的使用时长小于预设时间长度(tA<At ), 则前台应用 A的使用参数为 1 , 记为 Ao; 如果前台应用 A的使用时长大于或等于预设时间 长度(tA>=A t ), 则前台应用 A的使用参数为 n , 所述前台应用 A被记为 {Ao、 Ai An} , 其中" =「^ /Δ 。
假设 At=3分钟, 依据上述表 1可知, 则前台应用 A的使用参数为 2 , 记为 Αο、 Αΰ 前台应用 Β的使用参数为 1 , 记为 Β0; 然后前台应用 C的使用参数为 1 , 记为 Co; 接着前台应用 Α的使用参数为 3 , 记为 Ao、 A1 ¾ A2; 随后前台应 用 B的使用参数为 1 , 记为 Bo等。 其中, 当移动终端 1处于待机状态、 未激活 状态或者在移动终端的前台中所执行的应用为系统服务时, 所述使用参数计算 单元 112可以依据预设时间长度的设定计算使用参数, 记为 0, 依据表 1可知, 在 10:34至 10:42之间, 移动终端 1处于待机状态、 未激活状态或者在移动终端 的前台中所执行的应用为系统服务, 依据 At=3分钟, 则使用参数为 3 , 记为 0、 0、 0。
序列生成单元 113 ,用于根据所述至少一个前台应用中各前台应用的使用参 数, 釆用时间顺序对所述至少一个前台应用进行排序, 生成应用序列;
具体实现中, 所述序列生成单元 113 根据所述至少一个前台应用中各前台 应用的使用参数, 釆用时间顺序对所述至少一个前台应用进行排序, 生成应用 序列。
以上述表 1数据为例, 所述序列生成单元 113根据前台应用的使用参数, 釆用时间顺序进行排序, 可以生成以下序列:
Ao->Ai ->B0->C0->Ao->Ai ->A2->B0->Ao->Ai ->B0->B i ->0 ->0->0 ->C0
应用确定模块 12, 用于根据所述应用序列确定预测应用;
具体实现中, 所述应用确定模块 12在应用序列中截取前驱序列以及与所述 前驱序列相匹配的迁移序列, 并分别统计所述前驱序列和所述迁移序列在所述 应用序列中出现的次数, 所述应用确定模块 12根据所述前驱序列出现的次数和 所述迁移序列出现的次数获取与所述前驱序列和所述迁移序列相关联的待预测 应用的迁移概率, 并根据所获取的所有待预测应用确定所述预测应用。
具体的,请一并参见图 5 , 为本发明实施例提供了应用确定模块的结构示意 图。 如图 5所示, 所述应用确定模块 12包括:
序列截取单元 121 ,用于在所述应用序列中截取前驱序列和与所述前驱序列 相匹配的迁移序列;
具体实现中,移动终端 1中可以预设影响长度为 L,所述预设影响长度为移 动终端对预测应用进行判断的前驱序列的长度, 可以由运营商或出厂商自行设 定。 所述迁移序列由待预测应用和前驱序列组成, 所述前驱序列的长度为 L, 则 所述与所述前驱序列相匹配的迁移序列的长度为 L+1 , 所述前驱序列和所述迁 移序列中的前 L个因子相同, 所述待预测应用为所述迁移序列的最后一个因子, 其中, 所述 L为正整数。
所述序列截取单元 121 根据所述预设影响长度, 在所述应用序列中截取前 驱序列和与所述前驱序列相匹配的迁移序列。
假设预设影响长度 L=2,依据序列生成单元 113生成的应用序列可截取的前 驱序列包括: AoA^ AjBo, AiA2、 A2B。等, 可截取的迁移序列包括: AoA Bo AoAiA2, AjBoCo, A^Bo等。
次数获取单元 122,用于分别获取前驱序列在所述应用序列中出现的次数和 迁移序列在所述应用序列中出现的次数;
具体实现中, 所述次数获取单元 122对所述序列截取单元 121所截取的前 驱序列和迁移序列进行统计, 获取前驱序列在所述应用序列中出现的次数和迁 移序列在所述应用序列中出现的次数。 依据所述序列生成单元 113生成的应用序列和所述序列截取单元 121所截 取的前驱序列和迁移序列, 所述次数获取单元 122可以分别对前驱序列和迁移 序列进行统计, 生成上述表 2和表 3。
应用确定单元 123 ,用于根据所述前驱序列出现的次数和所述迁移序列出现 的次数确定预测应用;
具体实现中, 所述应用确定单元 123 可以计算所述前驱序列出现的次数和 所述迁移序列出现的次数的比值, 将所述比值作为所述前驱序列和所述迁移序 列相关联的待预测应用的迁移概率, 所述迁移概率为在以所述前驱序列为前提 条件, 所述待预测应用将被使用的概率, 所述应用确定单元 123 将所述迁移概 率大于预设阔值的待预测应用确定为预测应用。
以上述表 2和表 3的数据为例, 所述应用确定单元 123通过分别计算所述 前驱序列出现的次数和所述迁移序列出现的次数的比值, 可以获得上述表 4 , 将 所述比值作为所述前驱序列和所述迁移序列相关联的待预测应用的迁移概率, 例如: 参见上述表 4, 前驱序列为 AoAi , 与所述前驱序列相匹配的迁移序列为 ΑοΑιΑ2, 则可以获取与 AoA^o AoA Az相关联的待预测应用的标识为 A2, 即以 ΑοΑ为前提条件, 将被使用的待预测应用为 A, 将 AoA^。 AQA AZ得出的概率 0.17作为待预测应用 A的迁移概率。
移动终端 1可以预设阔值, 所述应用确定单元 123获取迁移概率大于预设 阔值的待预测应用, 将所述待预测应用确定为预测应用, 例如: 参见表 4, 假设 预设阔值为 0.5 , 则所述应用确定单元 123确定迁移概率大于预设阔值的待预测 应用为 B , 将所述待预测应用 B确定为预测应用。
具体的,请一并参见图 6 , 为本发明实施例提供了应用确定单元的结构示意 图。 如图 6所示, 所述应用确定单元 123包括:
比值计算子单元 1231 , 用于计算所述前驱序列出现的次数和所述迁移序列 出现的次数的比值;
具体实现中, 所述比值计算子单元 1231通过分别计算所述前驱序列出现的 次数和所述迁移序列出现的次数的比值, 可以生成上述表 4。
概率获取子单元 1232 , 用于将所述比值作为所述前驱序列和所述迁移序列 相关联的预测应用的迁移概率;
具体实现中, 所述概率获取子单元 1232将所述比值作为所述前驱序列和所 述迁移序列相关联的待预测应用的迁移概率, 例如: 参见上述表 4 , 前驱序列为 AoAj , 与所述前驱序列相匹配的迁移序列为 AoA Az , 则可以获取与 ΑοΑ 和 AQA AZ相关联的待预测应用的标识为 A2 , 即以 AQA 为前提条件, 将被使用的 待预测应用为 A, 将 ΑοΑ^ AQA AZ得出的概率 0.17作为待预测应用 A的迁移 概率。
应用确定子单元 1233 , 用于将所述迁移概率大于预设阔值的应用确定为预 测应用;
具体实现中, 移动终端 1可以预设阔值, 所述应用确定子单元 1233获取迁 移概率大于预设阔值的待预测应用, 将所述待预测应用确定为预测应用, 例如: 参见表 4 ,假设预设阔值为 0.5 , 则所述应用确定子单元 1233确定迁移概率大于 预设阔值的待预测应用为 B , 将所述待预测应用 B确定为预测应用。
内容获取输出模块 13 , 用于获取与所述预测应用相关联的推荐内容, 并输 出所述推荐内容;
具体实现中, 所述内容获取输出模块 13根据确定的预测应用, 获取所述预 测应用对应的应用类型, 优选的, 所述内容获取输出模块 13根据所述应用类型 向服务器请求相关联的推荐内容, 获取服务器发送的所述推荐内容, 对所述推 荐内容进行输出显示。
具体的,请一并参见图 7 , 为本发明实施例提供了内容获取输出模块的结构 示意图。 如图 7所示, 所述内容获取输出模块 13包括:
类型获取单元 131 , 用于获取所述预测应用对应的应用类型;
具体实现中, 所述类型获取单元 131 获取所述迁移概率大于预设阔值的预 测应用对应的应用类型。
内容获取输出单元 132 , 用于获取与所述应用类型相关联的推荐内容, 并输 出所述推荐内容;
具体实现中, 所述内容获取输出单元 132根据所述应用类型向服务器请求 相关联的推荐内容, 获取与所述应用类型相关联的推荐内容, 并输出所述推荐 内容。
在本发明实施例中, 通过对前台应用的使用信息设置应用序列, 根据应用 序列确定预测应用。 由前台应用的使用信息确定预测应用, 可以针对不同的移 动终端的使用情况, 预测用户即将使用的应用, 提高推荐内容的针对性, 减少 了不准确的推荐内容对用户造成的影响, 使得移动终端推荐的内容更具备人性 化的特点, 并且移动终端实时对预测应用进行获取, 可以在打开下一应用前或 者启动下一应用时, 对推荐内容进行输出显示, 可以引导用户在打开下一应用 时, 浏览推荐内容, 避免了由于网络状况不佳, 导致移动终端在用户使用该下 一应用的过程中, 输出推荐内容对用户造成干扰, 优化了对推荐内容进行输出 的推荐时机, 提升了移动终端的智能性, 提升了用户的体验。 请参见图 8, 为本发明实施例提供了另一种移动终端的结构示意图。 如图 8 所示, 本发明实施例的所述移动终端可以包括: 处理器 401、 存储器 402和通信 接口 403。存储器 402用于存储程序代码。 处理器 401用于执行存储器 402中存 储的程序代码。 本发明实施例中, 存储器 402存储有程序代码, 处理器 401用 于执行该程序代码, 包括执行如下操作: 监听前台应用的使用信息, 并根据所 述前台应用的使用信息设置应用序列; 根据所述应用序列确定预测应用; 获取 与所述预测应用相关联的推荐内容, 并输出所述推荐内容。 通信接口 403 , 用于 与外部设备通信, 如与其它终端通信。 其中, 处理器 401根据存储器 402中的 程序代码对通信接口 403接收到的消息进行处理, 并通过通信接口 403与外部 设备交互。 处理器 401可以是中央处理器(central processing unit, CPU )、 专用 集成电路 ( application-specific integrated circuit, ASIC )等。 其中, 本实施例中的 移动终端可以包括总线 404。 处理器 401、 存储器 402以及通信接口 403之间可 通过总线 404连接并通信。其中,存储器 402可以包括:随机存取存储器( random access memory, RAM ), 只读存 4诸器 ( read-only memory, ROM ), 磁盘等具有存 储功能的实体。 本发明实施例中的呼叫上下文可緩存在 RAM中。
在本发明实施例中, 通过对前台应用的使用信息设置应用序列, 根据应用 序列确定预测应用。 由前台应用的使用信息确定预测应用, 可以针对不同的移 动终端的使用情况, 预测用户即将使用的应用, 提高推荐内容的针对性, 减少 了不准确的推荐内容对用户造成的影响, 提升了移动终端的智能性, 使得移动 终端推荐的内容更具备人性化的特点, 提升了用户的体验。
通过以上的实施方式的描述, 所属领域的技术人员可以清楚地了解到本发 明可以用硬件实现, 或固件实现, 或它们的组合方式来实现。 当使用软件实现 时, 可以将上述功能存储在计算机可读介质中或作为计算机可读介质上的一个 或多个指令或代码进行传输。 计算机可读介质包括计算机存储介质和通信介质 , 其中通信介质包括便于从一个地方向另一个地方传送计算机程序的任何介质。 存储介质可以是计算机能够存取的任何可用介质。 以此为例但不限于: 计算机 可读介质可以包括 RAM、 ROM, EEPROM、 CD-ROM或其他光盘存储、 磁盘 存储介质或者其他磁存储设备、 或者能够用于携带或存储具有指令或数据结构 形式的期望的程序代码并能够由计算机存取的任何其他介质。 此外。 任何连接 可以适当的成为计算机可读介质。 例如, 如果软件是使用同轴电缆、 光纤光缆、 双绞线、 数字用户线(DSL )或者诸如红外线、 无线电和微波之类的无线技术从 网站、 服务器或者其他远程源传输的, 那么同轴电缆、 光纤光缆、 双绞线、 DSL 或者诸如红外线、 无线和微波之类的无线技术包括在所属介质的定影中。 如本 发明所使用的, 盘(Disk )和碟(disc ) 包括压缩光碟(CD )、 激光碟、 光碟、 数字通用光碟(DVD )、 软盘和蓝光光碟, 其中盘通常磁性的复制数据, 而碟则 用激光来光学的复制数据。 上面的组合也应当包括在计算机可读介质的保护范 围之内。
以上所揭露的仅为本发明较佳实施例而已, 当然不能以此来限定本发明之 权利范围, 因此依本发明权利要求所作的等同变化, 仍属本发明所涵盖的范围。

Claims

1、 一种内容推荐的方法, 其特征在于, 包括:
监听前台应用的使用信息, 并根据所述前台应用的使用信息设置应用序列; 才艮据所述应用序列确定预测应用;
获取与所述预测应用相关联的推荐内容, 并输出所述推荐内容。
2、 根据权利要求 1所述的方法, 其特征在于, 所述监听前台应用的使用信 息, 并根据所述前台应用的使用信息设置应用序列, 包括:
监听至少一个前台应用的使用信息, 获取至少一个前台应用中各前台应用 的使用时长, 所述使用信息包括所述前台应用的标识、 所述前台应用的开始使 用时刻和所述前台应用的退出前台时刻;
根据所述至少一个前台应用中各前台应用的使用时长, 计算所述至少一个 前台应用中各前台应用的使用参数;
根据所述至少一个前台应用中各前台应用的使用参数, 釆用时间顺序对所 述至少一个前台应用进行排序, 生成应用序列。
3、 根据权利要求 1或 2所述的方法, 其特征在于, 所述根据所述应用序列 确定预测应用, 包括:
在所述应用序列中截取前驱序列和与所述前驱序列相匹配的迁移序列; 分别获取前驱序列在所述应用序列中出现的次数和迁移序列在所述应用序 列中出现的次数;
根据所述前驱序列出现的次数和所述迁移序列出现的次数确定预测应用。
4、 根据权利要求 3所述的方法, 其特征在于, 所述根据所述前驱序列出现 的次数和所述迁移序列出现的次数确定预测应用, 包括:
计算所述前驱序列出现的次数和所述迁移序列出现的次数的比值; 将所述比值作为所述前驱序列和所述迁移序列相关联的待预测应用的迁移 概率; 将所述迁移概率大于预设阔值的待预测应用确定为预测应用。
5、 根据权利要求 4所述的方法, 其特征在于, 所述前驱序列的长度为 L, 与所述前驱序列相匹配的所述迁移序列的长度为 L+1 , 且所述前驱序列和所述 迁移序列中的前 L个因子相同, 所述待预测应用为所述迁移序列的最后一个因 子, 其中, 所述 L为正整数。
6、 根据权利要求 1或 2所述的方法, 其特征在于, 所述获取与所述预测应 用相关联的推荐内容, 并输出所述推荐内容, 包括:
获取所述预测应用对应的应用类型;
获取与所述应用类型相关联的推荐内容, 并输出所述推荐内容。
7、 一种移动终端, 其特征在于, 包括:
序列设置模块, 用于监听前台应用的使用信息, 并根据所述前台应用的使 用信息设置应用序列;
应用确定模块, 用于根据所述应用序列确定预测应用;
内容获取输出模块, 用于获取与所述预测应用相关联的推荐内容, 并输出 所述推荐内容。
8、根据权利要求 7所述的移动终端, 其特征在于, 所述序列设置模块包括: 应用监听获取单元, 用于监听至少一个前台应用的使用信息, 获取至少一 个前台应用中各前台应用的使用时长, 所述使用信息包括所述前台应用的标识、 所述前台应用的开始使用时刻和所述前台应用的退出前台时刻;
参数计算单元, 用于根据所述至少一个前台应用中各前台应用的使用时长, 计算所述至少一个前台应用中各前台应用的使用参数;
序列生成单元, 用于根据所述至少一个前台应用中各前台应用的使用参数, 釆用时间顺序对所述至少一个前台应用进行排序, 生成应用序列。
9、 根据权利要求 7或 8所述的移动终端, 其特征在于, 所述应用确定模块 包括: 序列截取单元, 用于在所述应用序列中截取前驱序列和与所述前驱序列相 匹配的迁移序列;
次数获取单元, 用于分别获取前驱序列在所述应用序列中出现的次数和迁 移序列在所述应用序列中出现的次数;
应用确定单元, 用于根据所述前驱序列出现的次数和所述迁移序列出现的 次数确定预测应用。
10、 根据权利要求 9 所述的移动终端, 其特征在于, 所述应用确定单元包 括:
比值计算子单元, 用于计算所述前驱序列出现的次数和所述迁移序列出现 的次数的比值;
概率获取子单元, 用于将所述比值作为所述前驱序列和所述迁移序列相关 联的待预测应用的迁移概率;
应用确定子单元, 用于将所述迁移概率大于预设阔值的待预测确定为预测 应用。
11、 根据权利要求 10所述的移动终端, 其特征在于, 所述前驱序列的长度 为 L, 与所述前驱序列相匹配的所述迁移序列的长度为 L+1 ,且所述前驱序列和 所述迁移序列中的前 L个因子相同, 所述待预测应用为所述迁移序列的最后一 个因子, 其中, 所述 L为正整数。
12、 根据权利要求 7或 8所述的移动终端, 其特征在于, 所述内容获取输 出模块包括:
类型获取单元, 用于获取所述预测应用对应的应用类型;
内容获取输出单元, 用于获取与所述应用类型相关联的推荐内容, 并输出 所述推荐内容。
PCT/CN2014/085908 2013-09-06 2014-09-04 一种内容推荐的方法及移动终端 WO2015032334A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201310404828.9A CN104427118B (zh) 2013-09-06 2013-09-06 一种内容推荐的方法及移动终端
CN201310404828.9 2013-09-06

Publications (1)

Publication Number Publication Date
WO2015032334A1 true WO2015032334A1 (zh) 2015-03-12

Family

ID=52627817

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2014/085908 WO2015032334A1 (zh) 2013-09-06 2014-09-04 一种内容推荐的方法及移动终端

Country Status (2)

Country Link
CN (1) CN104427118B (zh)
WO (1) WO2015032334A1 (zh)

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104750798B (zh) * 2015-03-19 2020-09-29 腾讯科技(深圳)有限公司 一种应用程序的推荐方法和装置
CN105528659A (zh) * 2016-01-27 2016-04-27 浙江大学 一种基于序列模式结合时间上下文的移动终端app使用预测方法
CN110020133B (zh) * 2017-11-07 2023-04-07 腾讯科技(深圳)有限公司 内容推荐处理方法和装置、计算机设备和存储介质
US10885912B2 (en) 2018-11-13 2021-01-05 Motorola Solutions, Inc. Methods and systems for providing a corrected voice command
CN112883275B (zh) * 2021-03-17 2024-01-19 北京乐我无限科技有限责任公司 一种直播间推荐方法、装置、服务器及介质

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101236563A (zh) * 2008-02-01 2008-08-06 刘峰 智能个性化服务网站构造方法
CN102387461A (zh) * 2011-10-18 2012-03-21 北京佳信汇通科技有限公司 一种移动数据业务推荐方法、装置和系统
CN102567511A (zh) * 2011-12-27 2012-07-11 奇智软件(北京)有限公司 一种应用自动推荐的方法及装置
CN103198418A (zh) * 2013-03-15 2013-07-10 北京亿赞普网络技术有限公司 一种应用推荐方法和系统
CN103279384A (zh) * 2013-05-31 2013-09-04 林吓洪 一种用户上报和共享应用程序的方法及装置

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4698281B2 (ja) * 2005-05-09 2011-06-08 ソニー・エリクソン・モバイルコミュニケーションズ株式会社 携帯端末、情報推奨方法及びプログラム
CN102130933B (zh) * 2010-01-13 2014-05-21 中国移动通信集团公司 一种基于移动互联网的推荐方法、系统和设备
JP5445339B2 (ja) * 2010-06-08 2014-03-19 ソニー株式会社 コンテンツ推薦装置およびコンテンツ推薦方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101236563A (zh) * 2008-02-01 2008-08-06 刘峰 智能个性化服务网站构造方法
CN102387461A (zh) * 2011-10-18 2012-03-21 北京佳信汇通科技有限公司 一种移动数据业务推荐方法、装置和系统
CN102567511A (zh) * 2011-12-27 2012-07-11 奇智软件(北京)有限公司 一种应用自动推荐的方法及装置
CN103198418A (zh) * 2013-03-15 2013-07-10 北京亿赞普网络技术有限公司 一种应用推荐方法和系统
CN103279384A (zh) * 2013-05-31 2013-09-04 林吓洪 一种用户上报和共享应用程序的方法及装置

Also Published As

Publication number Publication date
CN104427118B (zh) 2017-02-01
CN104427118A (zh) 2015-03-18

Similar Documents

Publication Publication Date Title
CN109152095B (zh) 用于终端的无线网络连接方法
CN111240837B (zh) 资源配置方法、装置、终端及存储介质
US9704503B2 (en) Command handling method, apparatus, and system
WO2020094036A1 (zh) 用于终端的无线网络连接方法
WO2015055081A1 (en) Method, apparatus and mobile terminal for browser based video playback
WO2015032334A1 (zh) 一种内容推荐的方法及移动终端
CN111064987B (zh) 信息展示方法、装置及电子设备
CN111447107B (zh) 网络状态确定方法、装置、存储介质及电子设备
CN109493852A (zh) 一种语音识别的评测方法及装置
US11809380B2 (en) Information sharing method, apparatus, electronic device, and storage medium
CN108900855B (zh) 直播内容录制方法、装置、计算机可读存储介质及服务器
US20230247257A1 (en) Broadcasting method and device for live broadcast
CN112311656B (zh) 消息聚合、展示方法、装置、电子设备和计算机可读介质
CN109582274B (zh) 音量调节方法、装置、电子设备及计算机可读存储介质
EP4113985A1 (en) Multimedia conference data processing method and apparatus, and electronic device
WO2023116219A1 (zh) Cdn节点分配方法、装置、电子设备、介质及程序产品
CN112312225B (zh) 信息展示方法、装置、电子设备和可读介质
CN110311963B (zh) 消息推送方法、装置、计算机设备及计算机可读存储介质
WO2022057727A1 (zh) 网络质量确定方法、装置、电子设备和可读存储介质
CN112291602B (zh) 视频播放方法、电子设备及存储介质
CN112566262B (zh) 数据处理方法及装置、通信设备及存储介质
CN110389805B (zh) 一种信息展示方法、装置、设备及存储介质
CN112149019A (zh) 用于显示信息的方法、装置、电子设备和计算机可读介质
CN114063795A (zh) 一种交互方法、装置、电子设备和存储介质
CN110401841B (zh) 一种直播间消息的解析方法、装置、设备及存储介质

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 14842352

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 14842352

Country of ref document: EP

Kind code of ref document: A1