SOFTWARE RECOMMENDING METHOD AND RECOMMENDING SYSTEM

• US 20140344254A1
• Filed: 12/12/2012
• Published: 11/20/2014
• Est. Priority Date: 12/14/2011
• Status: Active Grant

• Current Assignee: Beijing Qihu Technology Co. Ltd. (360 Security Technology, Inc.)
• Original Assignee: Beijing Qihoo Technology Company Limited
• Inventors: Pu, Xin, Tian, Ye, Liu, Yiping
• Granted Patent: US 9,886,512 B2
• Time in Patent Office: Days
• US Class Current: 707/722
• 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 already installed and/or web application already run on a client;

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; and

providing a corresponding software recommending mechanism according to the personalization data of the client user.

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 personality data comprises:

providing a key word related to the personality data, and providing a software recommendation list corresponding to the key word.

6. 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.

7. The method according to claim 1, wherein the detecting software already installed on a client comprises:

obtaining a list of software already installed on the client by detecting key values in a registration table of the client.

8. The method according to claim 1, wherein the detecting web application already run on a client 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.

9. The method according to claim 1, wherein the analyzing software already installed on the client and obtaining age data of the 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 software already installed on the client; and

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

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

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

or,

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

11. The method according to claim 1, wherein the analyzing software already installed on the client and obtaining personality data of the client user, comprises:

looking up the software analysis library and determining software classes to which each software already installed on the client belongs to;

making statistics of the number of software already installed on the client in each software class;

when the number of software already installed on the client in a certain software class is greater than or equal to a preset number in the software class, obtaining the personality data corresponding to the software class;

synthesizing personality data corresponding to the plurality of software classes in the same client to obtain the personality data of the client user.

12. The method according to claim 11, wherein before synthesizing personality data corresponding to the plurality of software classes in the same client, 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 is a special software in the software analysis library, obtaining the personality data corresponding to the special software.

13. The method according to claim 1, wherein the analyzing software already installed on the client and obtaining gender data of the client user, 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.

14. A software recommending system, comprising:

a software detecting module configured to detect software already installed and/or web application already run on a client;

a software analyzing module configured to analyze software already installed and/or web application already run on the client, and obtain personalization data of the client user, wherein the personalization data include age data, and/or personality data, and/or gender data; and

a software recommending module configured to provide a corresponding software recommending mechanism according to the personalization data of the client user.

15. The system according to claim 14, wherein

the personality data comprises information describing personality and hobbies.

16. The system according to claim 14, wherein providing a corresponding software recommending mechanism by the software recommending module 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.

17. The system according to claim 14, wherein

based on age data of the client user, the software recommending module provides a software recommendation list with age modified, the recommended software list comprises an age-fall software recommendation list and/or an age-rise software recommendation list;

based on the client user'"'"'s personality data, the software recommending module provides a key word related to the personality data, and a software recommendation list is provided corresponding to the key word; and

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

18. The system according to claim 14, wherein

the software detecting module obtains a list of software already installed on the client by detecting key values in a registration table of the client.

19. The system according to claim 14, wherein

the software detecting module obtains web applications run by a browser by detecting browser events;

and/or, obtains web applications run by a startup program by detecting the startup program installed on the client.

20. (canceled)

21. (canceled)

22. (canceled)

23. (canceled)

24. (canceled)

25. (canceled)

26. 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 already installed and/or web application already run on a client;

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; and

providing a corresponding software recommending mechanism according to the personalization data of the client user.