Methods and apparatus for serving relevant advertisements

• US 10,198,746 B2
• Filed: 09/14/2017
• Issued: 02/05/2019
• Est. Priority Date: 09/24/2002
• Status: Expired due to Term

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google LLC (Alphabet Inc.)
• Inventors: Dean, Jeffrey Adgate, Harik, Georges, Buchheit, Paul
• Primary Examiner(s): Alam, Shahid A
• Application Number:
  US15/704,445
• Publication Number:
  US 20180005266A1
• Time in Patent Office: 509 Days
• Field of Search: 707748
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0242 Determining effectiveness of advertisements
• CPC Class Codes: G06Q 30/0251 Targeted advertisements
• CPC Class Codes: G06Q 30/0256 User search
• CPC Class Codes: G06Q 30/0263 based upon Internet or website rating
• CPC Class Codes: Y10S 707/944 Business related

Abstract

The relevance of advertisements to a user'"'"'s interests is improved. In one implementation, the content of a web page is analyzed to determine a list of one or more topics associated with that web page. An advertisement is considered to be relevant to that web page if it is associated with keywords belonging to the list of one or more topics. One or more of these relevant advertisements may be provided for rendering in conjunction with the web page or related web pages.

Claims

1. A computer implemented method comprising:

obtaining a set of rules that specify weights for each term of a plurality of terms extracted from a resource, with a weight of a term being a function of a frequency of the term in the resource;

extracting a plurality of terms from a target resource;

generating, based on the set of rules, a term vector that represents weights for the plurality of terms that appear within the target resource;

determining that one or more terms of the term vector each have one or more respective weights that exceed a defined threshold;

identifying targeting data for a content item that is available for presentation with various different resources;

determining that the content item is relevant to the target resource, the determining including;

comparing the identified targeting data for the content item to the determined one or more terms of the term vector for the target resource; and

determining, based on the comparison, that the identified targeting data for the content item and the determined one or more terms of the term vector have a degree of similarity that is greater than a similarity threshold; and

serving the content item based on determining that the identified targeting data for the content item has the degree of similarity to the determined one or more terms of the term vector for the target resource.

2. The computer implemented method of claim 1, wherein the identified targeting data is provided by a content provider for the content item and comprises at least one of (A) a keyword and (B) a phrase, and wherein the identified targeting data for the content item is received via a content item campaign entry and management component of a server system.

3. The computer implemented method of claim 1, further comprising determining one or more topics that correspond to the target resource by determining that one or more terms of the term vector each have one or more respective weights that exceed a defined threshold.

4. The computer implemented method of claim 3, wherein the one or more topics corresponding to the target resource comprise at least one topic from another resource linked to the target resource.

5. The computer implemented method of claim 3, wherein the one or more topics corresponding to the target resource comprise anchor text in a link from another resource to the target resource.

6. The computer implemented method of claim 3, wherein the one or more topics corresponding to the target resource comprise text from queries to a search engine that returned a search result comprising the target resource.

7. The computer implemented method of claim 3, wherein the one or more topics corresponding to the target resource comprise text from queries to a search engine, wherein the search engine is configured to return a search result including the target resource, the search result being subsequently selectable by a user.

8. A non-transitory computer readable medium storing instructions that are executable by one or more processors to perform operations comprising:

obtaining a set of rules that specify weights for each term of a plurality of terms extracted from a resource, with a weight of a term being a function of a frequency of the term in the resource;

extracting a plurality of terms from a target resource;

generating, based on the set of rules, a term vector that represents weights for the plurality of terms that appear within the target resource;

determining that one or more terms of the term vector each have one or more respective weights that exceed a defined threshold;

identifying targeting data for a content item that is available for presentation with various different resources;

determining that the content item is relevant to the target resource, the determining including;

comparing the identified targeting data for the content item to the determined one or more terms of the term vector for the target resource; and

determining, based on the comparison, that the identified targeting data for the content item and the determined one or more terms of the term vector have a degree of similarity that is greater than a similarity threshold; and

serving the content item based on determining that the identified targeting data for the content item has the degree of similarity to the determined one or more terms of the term vector for the target resource.

9. The non-transitory computer readable medium of claim 8, wherein the identified targeting data is provided by a content provider for the content item and comprises at least one of (A) a keyword and (B) a phrase, and wherein the identified targeting data for the content item is received via a content item campaign entry and management component of a server system.

10. The non-transitory computer readable medium of claim 8, the operations further comprising determining one or more topics that correspond to the target resource by determining that one or more terms of the term vector each have one or more respective weights that exceed a defined threshold.

11. The non-transitory computer readable medium of claim 10, wherein the one or more topics corresponding to the target resource further comprise at least one topic from another resource linked to the target resource.

12. The non-transitory computer readable medium of claim 10, wherein the one or more topics corresponding to the target resource further comprise anchor text in a link from another resource to the target resource.

13. The non-transitory computer readable medium of claim 10, wherein the one or more topics corresponding to the target resource further comprise text from queries to a search engine that returned a search result including the target resource.

14. The non-transitory computer readable medium of claim 10, wherein the one or more topics corresponding to the target resource further comprise text from queries to a search engine, wherein the search engine returns a search result including the target resource, the search result being subsequently selectable by a user.

15. A computer implemented method comprising:

obtaining a set of rules that specify weights for each term of a plurality of terms extracted from a resource, with a weight of a term being a function of a frequency of the term in the resource;

extracting a plurality of terms from a target resource;

generating, based on the set of rules, a term vector that represents weights for the plurality of terms that appear within text of the target resource, wherein the weights are based on at least one of (A) whether the term appears in the text of the target resource more frequently, relative to one or more other terms that each appear less frequently, and (B) whether the term appears less frequently, relative to one or more other terms that each appear more frequently, across a collection of resources to which the target resource belongs;

identifying targeting data for a content item that is available for presentation with various different resources;

determining that the content item is relevant to the target resource, the determining including;

comparing the identified targeting data for the content item to the determined one or more terms of the term vector for the target resource; and

determining, based on the comparison, that the identified targeting data for the content item and the determined one or more terms of the term vector have a degree of similarity that is greater than a similarity threshold; and

serving the content item based on determining that the identified targeting data for the content item has the degree of similarity to the determined one or more terms of the term vector for the target resource.

16. The computer implemented method of claim 15, further comprising determining one or more topics that correspond to the target resource by determining that one or more terms of the term vector each have one or more respective weights that exceed a defined threshold.

17. The computer implemented method of claim 16, wherein comparing comprises scoring a similarity between the identified targeting data for the content item and the one or more topics that correspond to the target resource and determining that the content item is relevant to the target resource when the similarity is above a threshold score.

18. The computer implemented method of claim 15, wherein the identified targeting data is provided by a content provider for the content item and comprises at least one of (A) a keyword and (B) a phrase, and wherein the identified targeting data for the content item is received via a content item campaign entry and management component of a server system.

19. A system comprising:

one or more processing devices; and

at least one non transitory computer readable medium storing instructions operable to cause the one or more processing devices to perform operations comprising;

obtaining a set of rules that specify weights for each term of a plurality of terms extracted from a resource, with a weight of a term being a function of a frequency of the term in the resource;

extracting a plurality of terms from a target resource;

generating, based on the set of rules, a term vector that represents weights for the plurality of terms that appear within the target resource;

determining that one or more terms of the term vector each have one or more respective weights that exceed a defined threshold;

identifying targeting data for a content item that is available for presentation with various different resources;

determining that the content item is relevant to the target resource, the determining including;

comparing the identified targeting data for the content item to the determined one or more terms of the term vector for the target resource; and

determining, based on the comparison, that the identified targeting data for the content item and the determined one or more terms of the term vector have a degree of similarity that is greater than a similarity threshold; and

serving the content item based on determining that the identified targeting data for the content item has the degree of similarity to the determined one or more terms of the term vector for the target resource.

20. The system of claim 19, wherein the operations further comprise:

determining one or more topics that correspond to the target resource by determining that one or more terms of the term vector each have one or more respective weights that exceed a defined threshold.