Filtering Information Using Targeted Filtering Schemes

• US 20110010374A1
• Filed: 06/26/2009
• Published: 01/13/2011
• Est. Priority Date: 06/26/2008
• Status: Active Grant

• Current Assignee: Alibaba Group Holding Ltd.
• Original Assignee: Alibaba Group Holding Ltd.
• Inventors: Zhang, Zhenyu, Zhang, Zhenghua, Yang, Junjie, Ni, Liang
• Granted Patent: US 8,725,746 B2
• Time in Patent Office: Days
• US Class Current: 707/754
• CPC Class Codes: G06F 16/337 Profile generation, learning or modification
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 21/55 Detecting local intrusion or implementing counter-measures
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04L 51/04 Real-time or near real-time messaging, e.g. instant messaging [IM]
• CPC Class Codes: H04L 51/212 using filtering or selective blocking
• CPC Class Codes: H04L 63/0245 Filtering by information in the payload

Abstract

A method for filtering user information takes into account not only specific keywords in the user information, but also related user-characteristic data (e.g., user activity data), and allows targeted user characteristics to be determined from multiple aspects of user activities. In one aspect, the disclosed method adopts different filtering schemes for different types of targeted users to improve the recognition accuracy with respect to the target user information. The method determines a suitable filtering scheme using a correspondence relationship between the filtering scheme and keywords and user-characteristic data. The method uses modeling of sample users and multiple candidate filtering schemes to formulate targeted filtering scheme. An apparatus for implementing the method is also disclosed.

Claims

1. A method for filtering user information, the method comprising:

obtaining keywords and user-characteristic data of a user;

selecting a filtering scheme according to a correspondence relationship between the filtering scheme and the keywords and user-characteristic data, the filtering scheme being modeled to filter targeted users whose information has similar keywords and user-characteristic data; and

filtering the information of the user according to the selected filtering scheme.

2. The method as recited in claim 1, wherein the correspondence relationship between the filtering scheme and the keywords and user-characteristic data is configured using a procedure comprising:

setting targeted keywords and user-characteristic data from a data collection of targeted users;

generating user-characteristic parameters of a target user based on the targeted keywords and user-characteristic data;

filtering out anomalies in the user-characteristic parameters;

formulating the filtering scheme based on the filtered user-characteristic parameters; and

establishing a correspondence relationship between the filtering scheme and the targeted keywords and user-characteristic data.

3. The method as recited in claim 2, wherein generating user-characteristic parameters of the target user comprises:

identifying valid data in the extracted keywords and user-characteristic data;

selecting one or more sample users based on the valid data; and

obtaining the user-characteristic parameters of the target user based on the keywords and user-characteristic data of the one or more sample users.

4. The method as recited in claim 3, wherein obtaining the user-characteristic parameters of the target user comprises:

generating an aggregated variable of the target user based on a total frequency according to the keywords and user-characteristic data of the one or more sample users;

generating a ratio variable of the target user based on a send/receive ratio according to the user-characteristic data of the one or more sample users; and

generating an average variable based on an average frequency according to the keywords and user-characteristic data of the one or more sample users.

5. The method as recited in claim 2, wherein filtering out anomalies in the user-characteristic parameters comprises:

replacing a missing value in the user-characteristic parameters of the target user by a replacement value; and

replacing an irregular data in the user-characteristic parameters of the target user by a regular value.

6. The method as recited in claim 1, wherein the filtering scheme is modeled to filter targeted users using a procedure comprising:

setting keywords and user-characteristic data from a data collection of targeted users;

generating user-characteristic parameters of a target user based on the extracted keywords and user-characteristic data;

selecting one or more rule generation parameters from the user-characteristic parameters;

generating multiple filtering schemes based on the rule generation parameter; and

selecting the filtering scheme having the highest accuracy among the multiple filtering schemes to be the filtering scheme for the target user.

7. The method as recited in claim 1, further comprising:

scoring the user based on the selected filtering scheme, wherein the information of the user is filtered according to the filtering scheme only if the user'"'"'s exceeds a preset threshold.

8. The method as recited in claim 1, wherein the user-characteristic data comprises user activity data, user information data and network user-characteristic data.

9. The method as recited in claim 8, wherein

the user activity data includes one or more of the following types of information:

frequencies of characteristic word groups in information sent by the user within a time frame, the number of times the user sends/receives information, and the amount of information the user sends/receives;

the user information data includes one or more of the following types of information;

initial login time of the user, user activities after login, and the number of contacts of the user; and

the network user-characteristic data includes one or more of the following types of information;

the number of users within a same IP, and the number of users within a same machine ID.

10. An apparatus for filtering user information, the apparatus comprising:

a configuration module used for configuring a correspondence relationship between a targeted filtering scheme and targeted keywords and user-characteristic data from a data collection of targeted users;

an acquisition module used for acquiring keywords and user-characteristic data of a present user; and

a filtering module used for selecting the targeted filtering scheme according to the correspondence relationship based on the acquired keywords and user-characteristic data of the present user, and for filtering information of the present user according to the targeted filtering scheme.

11. The apparatus as recited in claim 10, wherein the configuration module comprises:

a parameter generation sub-module used for setting up the targeted keywords and user-characteristic data from the data collection of targeted users, and for generating user-characteristic parameters of a target user based on the targeted keywords and user-characteristic data;

a filtering sub-module used for filtering out anomalies in the user-characteristic parameters generated by the parameter generation sub-module; and

a rule generation sub-module used for generating the targeted filtering scheme for the target user based on the filtered user-characteristic parameters.

12. The apparatus as recited in claim 11, wherein the parameter generation sub-module comprises:

a recognition unit used for recognizing valid data in the targeted user-characteristic data;

a selection unit used for selecting one or more sample users based on the valid data; and

a computation unit used for computing the user-characteristic parameters of the target user based on the user-characteristic data of the one or more sample users selected by the selection unit.

13. The apparatus as recited in claim 11, wherein the filtering sub-module comprises:

a first filtering unit used for replacing a missing value in the user-characteristic parameters by a replacement value; and

a second filtering unit used for replacing an irregular value in the user-characteristic parameters by a regular value.

14. The apparatus as recited in claim 11, wherein the rule generation sub-module comprises:

a parameter selection unit used for selecting one or more rule generation parameters;

a rule computing unit used for generating multiple candidate filtering schemes based on the rule generation parameters selected by the parameter selection unit; and

a rule selection unit used for selecting a filtering scheme having the highest accuracy among the candidate filtering schemes to be the targeted filtering scheme of the target user.

15. The apparatus as recited in claim 11, wherein the filtering module comprises:

a searching sub-module used for finding the targeted filtering scheme according to the correspondence relationship based on the keywords and user-characteristic data of the present user; and

a filtering sub-module used for filtering the information of the present user according to the targeted filtering scheme.

16. The apparatus as recited in claim 15, wherein the filtering module further comprises:

a determination sub-module used for determining a score for the present user according to the target filtering scheme, and for triggering the filtering sub-module if the score of the present user exceeds a preset threshold.

17. The apparatus as recited in claim 10, and wherein the apparatus comprises a server computer.