METHOD AND APPARATUS FOR SCREENING PROMOTION KEYWORDS

• US 20150302476A1
• Filed: 04/21/2015
• Published: 10/22/2015
• Est. Priority Date: 04/22/2014
• Status: Abandoned Application

• Current Assignee: Alibaba Group Holding Ltd.
• Original Assignee: Alibaba Group Holding Ltd.
• Inventors: Wu, Kewen, Huang, Peng, Li, Bo, Lin, Feng, Huang, Kaiming
• Application Number:
  US14/692,586
• Publication Number:
  US 20150302476A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/0256 User search

Abstract

Candidate promotion keywords are selected. Features of the candidate promotion keywords are extracted. The features include at least one of a search engine feature, an effect feature of non-directed traffic, and a text feature. The features of the candidate promotion keywords are used as input data of a pre-established keyword screening model, and superior promotion keywords are obtained according to a prediction result of the keyword screening model.

Claims

1. A method, comprising:

selecting one or more candidate promotion keywords;

extracting one or more features of the candidate promotion keywords;

using the one or more features of the candidate promotion keywords as input data of a pre-established keyword screening model; and

obtaining one or more superior promotion keywords according to a prediction result of the keyword screening model.

2. The method of claim 1, wherein the selecting the candidate promotion keywords comprises:

selecting the candidate promotion keywords by using a search keyword of a merchant website or an expansion word of a promotion keyword that has been placed into a search engine.

3. The method of claim 1, wherein the features comprise a search engine feature, the search engine feature comprising a search volume or popular rate information of a respective candidate promotion keyword in the search engine.

4. The method of claim 1, wherein the features comprise an effect feature of non-directed traffic, the effect feature of non-directed traffic comprising a search volume, a page view, a click rate, or a trade volume of a respective candidate promotion keyword at a merchant web site.

5. The method of claim 1, wherein the features comprise a text feature, the text feature comprising a word feature, a semantic feature, or an industry feature of a respective candidate promotion keyword,

wherein:

the word feature comprises at least one of a smallest word segmentation unit, a quantity of smallest word segmentation units, and a character length included in the respective candidate promotion keyword;

the semantic feature comprises at least one of a head word, a product word, and a brand word included in the respective candidate promotion keyword; and

the industry feature comprises an industry category to which the respective candidate promotion keyword belongs.

6. The method of claim 1, wherein:

the one or more features comprises a bid feature; and

the extracting one or more features of the candidate promotion keywords comprises constructing the bid feature of a respective candidate promotion keyword according to a preset bid interval between a lowest bid and a highest bid to the respective candidate promotion keyword.

7. The method of claim 6, wherein the method further comprises determining a respective suggested bid price of a respective superior promotion keyword.

8. The method of claim 7, wherein the determining the respective suggested bid price of the respective superior promotion keyword comprises:

combining the bid feature of the respective superior promotion keyword; and

using a highest bid as the respective suggested bid price of the respective superior promotion keyword.

9. The method of claim 1, further comprising performing one or more filtering to the obtained superior promotion keywords, the filtering comprising at least one of:

removing, from the obtained superior promotion keywords, one or more promotion keywords that have been placed into a search engine; and

removing illegal keywords from the obtained superior promotion keywords according to a prohibited word black list of a merchant website or a prohibited word black list of the search engine.

10. The method of claim 1, further comprising establishing the keyword screening model, the establishing comprising:

using data of one or more promotion keywords that have been placed into a search engine as training samples;

determining, by using the data of the promotion keywords, return on investment for each of the promotion keywords;

labeling the training samples according to the return on investment for the each of the promotion keywords;

extracting the features of each of the promotion keywords in the training samples, the features being consistent with the extracted features of the candidate promotion keywords; and

training a classification model by using the extracted features and the labeled training samples to obtain the keyword screening model.

11. The method of claim 10, wherein the determining, by using the data of the promotion keywords, return on investment for the each of the promotion keywords comprises at least one of the following:

using a ratio of a traffic introduced into the merchant website by a respective promotion keyword through the search engine to a cost of the investment of the merchant for the respective promotion keyword as the return on investment for the respective promotion keyword;

using a ratio of advertising income introduced into the merchant by the respective promotion keyword through the search engine to a cost of the investment of the merchant for the respective promotion keyword as the return on investment for the respective promotion keyword; and

using a ratio of a trade volume introduced into the merchant by the respective promotion keyword through the search engine to a cost of the investment of the merchant for the respective promotion keyword as the return on investment for the respective promotion keyword.

12. The method of claim 10, wherein the labeling the training samples according to the return on investment for the each of the promotion keywords comprises:

in response to determining that the return on investment for a respective promotion keyword is greater than or equal to a preset first threshold, labeling the respective promotion keyword as a superior promotion keyword; and

in response to determining that the return on investment for the respective promotion keyword is less than a preset second threshold, labeling the respective promotion keyword as an inferior promotion keyword, the first threshold being greater than or equal to the second threshold.

13. The method of claim 12, wherein in response to determining that the first threshold is greater than the second threshold, the labeling the training samples according to the return on investment for the each of the promotion keywords further comprises:

in response to determining the return on investment for the respective promotion keyword is greater than or equal to the second threshold and is less than the first threshold, labeling the respective promotion keyword as a medium promotion keyword.

14. An apparatus, comprising:

a keyword selection unit that selects one or more candidate promotion keywords;

a feature extraction unit that extracts one or more features of the candidate promotion keywords; and

a keyword screening unit that uses the one or more features of the candidate promotion keywords as input data of a pre-established keyword screening model and obtains one or more superior promotion keywords according to a prediction result of the keyword screening model.

15. The apparatus of claim 14, wherein the keyword selection unit further selects the candidate promotion keywords by using a search keyword of a merchant website or an expansion word of a promotion keyword that has been placed into a search engine.

16. The apparatus of claim 14, wherein

the one or more features comprises a bid feature;

and

the feature extraction unit extracts one or more features of the candidate promotion keywords comprises constructing the bid feature of a respective candidate promotion keyword according to a preset bid interval between a lowest bid and a highest bid to the respective candidate promotion keyword.

17. The apparatus of claim 14, wherein the apparatus further comprises a bid price suggesting unit that combines the bid feature of a respective superior promotion keyword;

and using a highest bid as a respective suggested bid price of the respective superior promotion keyword.

18. The apparatus of claim 14, wherein the apparatus further comprises a keyword filtering unit that performs one or more filtering to the obtained superior promotion keywords, the filtering including at least one of:

removing, from the obtained superior promotion keywords, one or more promotion keywords that have been placed into a search engine; and

removing illegal keywords from the obtained superior promotion keywords according to a prohibited word black list of a merchant website or a prohibited word black list of the search engine.

19. The apparatus of any of claims 14, wherein the apparatus further comprises a screening model establishing unit that establishes the keyword screening model, the screening model establishing unit including:

a sample determination sub-unit that uses data of one or more promotion keywords that have been placed into a search engine as training samples;

a sample labeling sub-unit that determines, by using the data of the promotion keywords, return on investment for each of the promotion keywords and labels the training samples according to the return on investment for the each of the promotion keywords;

a feature extraction sub-unit that extracts the features of each of the promotion keywords in the training samples, the features being consistent with the extracted features of the candidate promotion keywords; and

a model training sub-unit that trains a classification model by using the extracted features and the labeled training samples to obtain the keyword screening model.

20. One or more memories having stored thereon computer-readable instructions executable by one or more processors to perform operations comprising:

selecting one or more candidate promotion keywords;

extracting one or more features of the candidate promotion keywords;

using the one or more features of the candidate promotion keywords as input data of a pre-established keyword screening model; and

obtaining one or more superior promotion keywords according to a prediction result of the keyword screening model.