Method and apparatus of generating update parameters and displaying correlated keywords

• US 8,676,811 B2
• Filed: 08/28/2007
• Issued: 03/18/2014
• Est. Priority Date: 04/10/2007
• Status: Active Grant

• Current Assignee: Alibaba Group Holding Ltd.
• Original Assignee: Alibaba Group Holding Ltd.
• Inventors: Yao, Yuanhu, Zhang, Tianji, Pan, Lei, Yang, Zhen
• Primary Examiner(s): Beausoliel, Jr., Robert
• Assistant Examiner(s): Arjomandi, Noosha
• Application Number:
  US12/594,930
• Publication Number:
  US 20100121860A1
• Time in Patent Office: 2,394 Days
• Field of Search: 707/748
• US Class Current: 707/748
• CPC Class Codes: G06F 16/3322 using system suggestions G06F16/3325 takes precedence
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536
• CPC Class Codes: G06F 16/90324 using system suggestions

Abstract

Provided is a method of generating updating parameters. The method obtains search keywords used by users within a predetermined time period; counts the search keywords to obtain primary keywords, related keywords, co-search frequencies of each primary keyword and the respective related keywords being searched together, and search frequencies of the primary keywords being searched alone; computes first feature values based on the search frequencies of the primary keywords being searched alone; and then computes second feature values based on the first feature values and the co-search frequencies of the primary keywords and the respective related keywords. The second feature values serve as updating parameters for determining displaying modes of the related keywords. An apparatus of generating updating parameters, and a method and an apparatus of displaying related keywords according to the updating parameters are also provided. The solution keeps abreast with the user trends to allow a better user experience and improve computing performance and efficiency. For a service provider, no special secret algorithm is needed, and the operation is easy with a low development cost.

Claims

1. A computer-implemented method comprising:

obtaining multiple words searched by one or more users within a predetermined period;

determining a primary keyword of the multiple words and a primary frequency that the primary keyword is searched;

determining one or more relevant keywords associated with the primary keyword and a related frequency that each of the one or more relevant keywords and the primary keyword are searched together;

computing one or more feature values for corresponding ones of the one or more relevant keywords based on the primary frequency and the related frequency;

storing the one or more relevant keywords, a feature value corresponding to each of the one or more relevant keywords, and the primary keyword to generate keyword information; and

updating the keyword information in a predetermined time period by;

removing a relevant keyword having a feature value less than a predetermined value; and

adding a relevant keyword having a feature value greater than the predetermined value;

receiving a query generated by a user, the query including the primary keyword; and

recommending a relevant keyword of the keyword based on the primary frequency of the primary keyword and the related frequency of the relevant keyword, wherein the recommending the relevant keyword comprises recommending the relevant keyword of the keyword when a feature value corresponding to the relevant keyword is greater than or equal to a predetermined threshold, the feature value being determined based on the primary frequency of the primary keyword and the related frequency of the relevant keyword.

2. The computer-implemented method as recited in claim 1, wherein the determining of the primary keyword of the multiple words comprises determining the primary keyword using an Apriori algorithm.

3. The computer-implemented method as recited in claim 1, wherein the computing of the one or more feature values comprises:

computing a correlation level of one of the one or more primary keywords and a corresponding relevant keyword based on the related frequency; and

computing the one or more feature values based on the primary frequency and the correlation level.

4. The computer-implemented method as recited in claim 1, further comprising:

determining a user type of the user, wherein the relevant keyword is searched by one user having one user type similar to the user type.

5. The computer-implemented method as recited in claim 1, wherein the primary frequency is not less than a predetermined popularity base value.

6. The computer-implemented method as recited in claim 1, further comprising filtering out one or more of the multiple words that fail to satisfy a filtering rule.

7. The computer-implemented method as recited in claim 1, wherein the one or more relevant keywords, a feature value corresponding to each of the one or more relevant keywords and the primary keyword comprising form a keyword information table.

8. An apparatus comprising:

one or more processors;

memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform acts comprising;

obtaining multiple words searched by one or more users within a predetermined period;

determining a primary keyword of the multiple words and a primary frequency that the primary keyword is searched;

determining one or more relevant keywords associated with the primary keyword and a related frequency that each of the one or more relevant keywords and the primary keyword are searched together;

computing one or more feature values for corresponding ones of the one or more relevant keywords based on the primary frequency and the related frequency;

storing the one or more relevant keywords, a feature value corresponding to each of the one or more relevant keywords, and the primary keyword to generate keyword information; and

updating the keyword information in a predetermined time period by;

removing a relevant keyword having a feature value less than a predetermined value; and

adding a relevant keyword having a feature value greater than the predetermined value;

receiving a query generated by a user, the query including the primary keyword; and

recommending a relevant keyword of the keyword based on the primary frequency of the primary keyword and the related frequency of the relevant keyword, wherein the recommending the relevant keyword comprises recommending the relevant keyword of the keyword when a feature value corresponding to the relevant keyword is greater than or equal to a predetermined threshold, the feature value being determined based on the primary frequency of the primary keyword and the related frequency of the relevant keyword.

9. The apparatus as recited in claim 8, wherein the determining of the primary keyword of the multiple words comprises determining the primary keyword using an Apriori algorithm.

10. The apparatus as recited in claim 8, wherein the computing of the one or more feature values comprises:

computing a correlation level of one of the one or more primary keywords and a corresponding relevant keyword based on the related frequency; and

computing the one or more feature values based on the primary frequency and the correlation level.

11. The apparatus as recited in claim 8, further comprising:

determining a user type of the user, wherein the relevant keyword is searched by one user having one user type similar to the user type.

12. The apparatus as recited in claim 8, wherein the primary frequency is not less than a predetermined popularity base value.

13. The apparatus as recited in claim 8, further comprising filtering out one or more of the multiple words that fail to satisfy a filtering rule.

14. The apparatus as recited in claim 8, wherein the one or more relevant keywords, a feature value corresponding to each of the one or more relevant keywords and the primary keyword comprising form a keyword information table.