AUTOMATIC CLASSIFICATION OF DISPLAY ADS USING AD IMAGES AND LANDING PAGES

• US 20120158525A1
• Filed: 12/20/2010
• Published: 06/21/2012
• Est. Priority Date: 12/20/2010
• Status: Active Grant

• Current Assignee: R2 Solutions LLC (Acacia Research Corporation)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Narayanan, Vijay K., Kae, Andrew, Kan, Kin Fai
• Granted Patent: US 8,732,014 B2
• Time in Patent Office: Days
• US Class Current: 705/14.73
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/08 Auctions
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

A system and method for automatically classifying ads into a taxonomy of categories, the method including: extracting text features from ad images using OCR (optical character recognition) techniques; identifying objects of interest from ad images using object detection and recognition techniques in computer vision; extracting text features from the web-page of the advertiser to which the user is re-directed when clicking the ad; training statistical models using the extracted features mentioned above as well as advertiser attributes from a historical dataset of ads labeled by human editors; and determining the relevant categories of unlabeled ads using the trained models.

Claims

1. A method for classifying ads automatically in a taxonomy of categories, the method comprising steps of:

extracting text features from ad images using optical character recognition (OCR) techniques;

identifying objects of interest from ad images using object detection and recognition techniques in computer vision;

extracting text features from the web-page of the advertiser that the user is redirected to when clicking the ad;

training statistical models using the extracted features as well as the advertiser attributes from a historical dataset of ads labeled by human editors; and

determining the relevant categories of unlabeled ads using the trained models.

2. The method of claim 1 further comprising:

facilitating manual editorial categorization by suggesting categories;

generating training features and targets in use interests modeling, and

generating attributes in match/rank algorithms used in ad selection algorithms.

3. A system of display ad categorization, the system comprising:

a processor device;

a storage device operably coupled with the processor device, said storage device comprising instructions that are executed by said processor device;

wherein the instructions cause a computer to perform a method comprising steps of;

reading an ad image and a landing page for an advertisement from the storage device;

extracting text features from an ad image using optical character recognition (OCR) techniques;

executing object detection and recognition to identify objects of interest from the ad image;

parsing the landing page to extract text features;

storing the extracted features from the ad image and the landing page in the storage device;

training statistical models using the extracted features as well as advertiser attributes from a historical dataset of ads labeled by human editors; and

determining the relevant categories of unlabeled ads using the trained models.