Software recommending method and recommending system

• US 9,886,512 B2
• Filed: 12/12/2012
• Issued: 02/06/2018
• Est. Priority Date: 12/14/2011
• Status: Active Grant

• Current Assignee: Beijing Qihu Technology Co. Ltd. (360 Security Technology, Inc.)
• Original Assignee: Beijing Qihu Technology Co. Ltd. (360 Security Technology, Inc.)
• Inventors: Pu, Xin, Tian, Ye, Liu, Yiping
• Primary Examiner(s): Morrison, Jay
• Assistant Examiner(s): Hoang, Ken
• Application Number:
  US14/364,962
• Publication Number:
  US 20140344254A1
• Time in Patent Office: 1,882 Days
• Field of Search: 709204, 709203, 709216, 709217, 709224, 706 46, 706 48, 706 12, 706 45, 706 50, 707E17009, 707E17014, 707722, 707736, 707749, 707758, 707769
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 8/61 Installation
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products

Abstract

Disclosed are a software recommending method and a software recommending system. The method comprises: detecting software already installed and/or web application already run on a client (101); analyzing software already installed and/or web application already run on the client, and obtaining personalization data of the client user, wherein the personalization data include age data, and/or personality data, and/or gender data (102); providing a corresponding software recommending mechanism according to the personalization data of the client user (103). This solution makes software recommendations for different personalities of different users, so that the recommendations are more targeted; and a user also does not need to search a variety of software for desired software through complicated operations.

Claims

1. A software recommending method, comprising:

detecting software that has been installed or has been run on a client computing device;

analyzing the software to obtain personality data, age data, or gender data associated with a client user of the computing device, wherein the personality data associated with the client user is determined based at least in part on at least one software classes of the software and personality description corresponding to the at least one software class, wherein the personality description corresponding to the at least one software class describes personality of a user who installs or runs a certain number of software in the at least one software class, wherein the analysing the software to obtain personality data associated with the client user of the computing device further comprises obtaining and synthesizing personality description corresponding to each of the at least one software class in response to a determination that a number of software among the software that has been installed or has been run on the client computing device in each of the at least one software class is greater than or equal to a corresponding threshold; and

providing a corresponding software recommending mechanism according to the personality data, age data, or gender data associated with the client user, wherein the providing a corresponding software recommending mechanism according to the personality data associated with the client user further comprises providing a key word related to the personality data, and providing a software recommendation list corresponding to the key word.

2. The method according to claim 1, wherein

the personality data comprises information describing personality and hobbies.

3. The method according to claim 1, wherein the providing a corresponding software recommending mechanism comprises:

providing a corresponding software recommendation list; and

/or

providing a corresponding software recommendation list and providing relevant operations of the recommended software, wherein the relevant operations include adjusting interface layout, and/or adjusting a size of font display, and/or adjusting interface display brightness.

4. The method according to claim 1, wherein the providing a corresponding software recommending mechanism based on age data of the client user comprises:

providing a software recommendation list with age modified, the recommended software list comprising an age-fall software recommendation list and/or an age-rise software recommendation list.

5. The method according to claim 1, wherein the providing a corresponding software recommending mechanism based on the client user'"'"'s gender data comprises:

providing a software recommendation list corresponding to the gender data.

6. The method according to claim 1, wherein the detecting software that has been installed or has been run on a client computing device comprises:

obtaining a list of the software by detecting key values in a registration table of the client computing device.

7. The method according to claim 1, wherein the detecting software that has been installed or run on a client computing device comprises:

obtaining web applications run by a browser by detecting browser events; and

/or,

obtaining web applications run by a startup program by detecting the startup program installed on the client computing device.

8. The method according to claim 1, wherein the analyzing software to obtain age data associated with a client user, comprises:

looking up a software analysis library in which an age score corresponding to each software is preset;

obtaining the age score corresponding to each of the software; and

calculating age data of the client user according to the age score corresponding to the software.

9. The method according to claim 8, wherein the calculating age data of the client user according to the age score corresponding to the software comprises:

accumulatively adding up a preset reference age data with the age scores corresponding to all of the software to obtain the client user'"'"'s age data;

or,

inputting the age scores corresponding to all of the software into a functional relation formula and calculating the client user'"'"'s age data.

10. The method according to claim 1, wherein the analyzing the software to obtain personality data of the client user, comprises:

looking up a software analysis library and determining software classes to which each of the software belongs to;

making statistics of, among the software, a number of software in each of the plurality of at least one software classes.

11. The method according to claim 10, wherein before synthesizing personality data corresponding to the plurality of software classes to obtain the personality data of the client user, the method further comprises:

looking up the software analysis library in which special software and personality data corresponding thereto are preset; and

when a certain software already installed on the client computing device is a special software in the software analysis library, obtaining the personality data corresponding to the special software.

12. The method according to claim 1, wherein the analysing the software to obtain gender data, comprises:

looking up the software analysis library in which a ratio of each software in two classes, namely, males and females, is preset respectively;

obtaining the respective ratios of each of the client-installed software in the two classes, namely, males and females; and

adding up the ratios of the client-installed software according to the two classes, namely, males and females, comparing the total ratios after the addition, and selecting a gender with a higher total ratio as the gender data of the client user.

13. A software recommending system, comprising:

at least a processor;

at least a memory communicatively coupled to the at least processor and bearing instructions that upon execution by the at least processor cause the system at least to;

detect software that has been installed or has been run on a client computing device;

analyze the software to obtain personality data, age data, or gender data associated with a client user of the client computing device, wherein the personality data associated with the client user is determined based at least in part on at least one software class of the software and personality description corresponding to the at least one software class, wherein the personality description corresponding to the at least one software class describes personality of a user who installs or runs a certain number of software in the at least one software class, wherein the analysing the software to obtain personality data associated with the client user of the computing device further comprises obtaining and synthesizing personality description corresponding to each of the at least one software class in response to a determination that a number of software among the software that has been installed or has been run on the client computing device in each of the at least one software class is greater than or equal to a corresponding threshold; and

provide a corresponding software recommending mechanism according to the personality data, age data, or gender data associated with the client user, wherein the instructions that upon execution by the at least processor cause the system to provide a corresponding software recommending mechanism according to the personality data associated with the client user further comprises instructions that upon execution by the at least processor cause the system to provide a key word related to the personality data, and provide a software recommendation list corresponding to the key word.

14. The system according to claim 13, wherein

the personality data comprises information describing personality and hobbies.

15. The system according to claim 13, wherein the at least memory further storing instructions that upon execution by the at least processor cause the system to:

provide a corresponding software recommendation list;

or

provide a corresponding software recommendation list and provide relevant operations of the recommended software, wherein the relevant operations include adjusting interface layout, adjusting a size of font display, or adjusting interface display brightness.

16. The system according to claim 13, wherein the at least memory further storing instructions that upon execution by the at least processor cause the system to:

based on age data of the client user, provide a software recommendation list with age modified, the recommended software list comprises an age-fall software recommendation list or an age-rise software recommendation list;

and

provide a software recommendation list corresponding to the gender data based on the client user'"'"'s gender data.

17. The system according to claim 13, wherein the at least memory further storing instructions that upon execution by the at least processor cause the system to:

obtain a list of the software by detecting key values in a registration table of the client computing device.

18. The system according to claim 13, wherein the at least memory further storing instructions that upon execution by the at least processor cause the system to:

obtain web applications run by a browser by detecting browser events;

or obtain web applications run by a startup program by detecting the startup program installed on the client computing device.

19. A computer readable medium which stores the computer program comprising a computer readable code, when the computer readable code is run on a server, the server executes a software recommending method, the method comprising:

detecting software that has been installed has been run on a client computing device;

analyzing the software installed to obtain personality data, age data, or gender data associated with a client user of the client computing device, wherein the personality data associated with the client user is determined based at least in part at least one software class of the software and personality description corresponding to the at least one software class, wherein the personality description corresponding to the at least one software class describes personality of a user who installs or runs a certain number of software in the at least one software class, wherein the analysing the software to obtain personality data associated with the client user of the computing device further comprises obtaining and synthesizing personality description corresponding to each of the at least one software class in response to a determination that a number of software among the software that has been installed or has been run on the client computing device in each of the at least one software class is greater than or equal to a corresponding threshold; and

providing a corresponding software recommending mechanism according to the personality data, age data, or gender data associated with the client user, wherein the providing a corresponding software recommending mechanism according to the personality data associated with the client user further comprises providing a key word related to the personality data, and providing a software recommendation list corresponding to the key word.