Methods and apparatus for serving relevant advertisements

• US 8,171,034 B2
• Filed: 03/22/2010
• Issued: 05/01/2012
• Est. Priority Date: 09/24/2002
• Status: Expired due to Term

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Dean, Jeffrey A., Harik, Georges R., Bucheit, Paul
• Primary Examiner(s): ALAM, SHAHID AL
• Application Number:
  US12/729,054
• Publication Number:
  US 20100174605A1
• Time in Patent Office: 771 Days
• Field of Search: 707723-730, 707/709, 707/711, 707/716, 707/748, 707/769, 705/14.25, 705/14.49, 705/26.1, 709/219, 715/205
• US Class Current: 707/748
• 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:

a) determining, by a server system including at least one processor on a network, a set of one or more topics for a target document by calculating weighted terms for the target document based on text within the target document, wherein the set of one or more topics includes only a defined number of those of the weighted terms with the highest weights among the weighted terms;

b) for each of a plurality of advertisements, determining, by the server system, whether the advertisement is relevant to the target document by analyzing a set of one or more topics of the advertisement, with respect to the set of one or more topics of the target document;

c) for each of the plurality of advertisements, making a serving determination, by the server system, using at least the determination of whether or not the advertisement is relevant to the target document; and

d) controlling, by the server system, serving of the advertisement for presentation to a user via a client device using the serving determination,

wherein the act of calculating weighted terms for the target document based on text within the target document includes determining the weight of each of the weighted terms based on at least one of (A) a frequency with which the term appears in the text of the target document, or (B) an infrequency with which the term appears across a collection of documents.

2. The computer-implemented method of claim 1 wherein analyzing a set of one or more topics, previously provided from an advertiser as targeting information for the advertisement, with respect to the set of one or more topics of the target document, includes scoring a similarity between the set of one or more topics of the advertisement and the set of one or more topics of target document.

3. The computer-implemented method of claim 1 wherein the set of one or more topics previously provided from an advertiser as targeting information for the advertisement include at least one of (A) a keyword and (B) a phrase, and wherein the set of one or more topics previously provided from an advertiser as targeting information for the advertisement were received via an ad campaign entry and management component of the server system.

4. The computer-implemented method of claim 1 wherein the set of one or more topics of the target document further includes at least one topic from another document linked to the target document.

5. The computer-implemented method of claim 1 wherein the set of one or more topics of the target document further includes at least one topic from another document linked from the target document.

6. The computer-implemented method of claim 1 wherein the set of one or more topics of the target document further includes anchor text in a link from another document to the target document.

7. The computer-implemented method of claim 1 wherein the set of one or more topics of the target document further includes text from queries to a search engine that returned a search result including the target document.

8. The computer-implemented method of claim 1 wherein the set of one or more topics of the target document further includes text from queries to a search engine that returned a search result including the target document, which was subsequently selected by a user.

9. A computer-implemented method comprising:

a) determining, by a server system including at least one processor on a network, a set of one or more topics for a target document by calculating weighted terms for the target document based on text within the target document, wherein the set of one or more topics contains only those of the weighted terms whose weight exceeds a defined threshold;

b) for each of a plurality of advertisements, determining, by the server system, whether the advertisement is relevant to the target document by analyzing a set of one or more topics of the advertisement, with respect to the set of one or more topics of the target document;

c) for each of the plurality of advertisements, making a serving determination, by the server system, using at least the determination of whether or not the advertisement is relevant to the target document; and

d) controlling, by the server system, serving of the advertisement for presentation to a user via a client device using the serving determination,

wherein the act of calculating weighted terms for the target document based on text within the target document includes determining the weight of each of the weighted terms based on at least one of (A) a frequency with which the term appears in the text of the target document, or (B) an infrequency with which the term appears across a collection of documents.