SYSTEM AND METHOD TO IDENTIFY CONTEXT-DEPENDENT TERM IMPORTANCE OF QUERIES FOR PREDICTING RELEVANT SEARCH ADVERTISEMENTS

• US 20110131205A1
• Filed: 11/28/2009
• Published: 06/02/2011
• Est. Priority Date: 11/28/2009
• Status: Abandoned Application

• Current Assignee: Oath Inc. (Verizon Communications Inc.)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Raghavan, Hema, Iyer, Rukmini, Manavoglu, Eren
• Application Number:
  US12/626,894
• Publication Number:
  US 20110131205A1
• Time in Patent Office: Days
• US Class Current: 707/728
• CPC Class Codes: G06F 16/3334 Selection or weighting of terms from queries, including natural language queries

Abstract

An improved system and method for identifying context-dependent term importance of queries is provided. A query term importance model is learned using supervised learning of context-dependent term importance for queries and is then applied for advertisement prediction using term importance weights of query terms as query features. For instance, a query term importance model for query rewriting may predict rewritten queries that match a query with term importance weights assigned as query features. Or a query term importance model for advertisement prediction may predict relevant advertisements for a query with term importance weights assigned as query features. In an embodiment, a sponsored advertisement selection engine selects sponsored advertisements scored by a query term importance engine that applies a query term importance model using term importance weights as query features and inverse document frequency weights as advertisement features to assign a relevance score.

Claims

1. A computer system for predicting relevant search advertisements, comprising:

a query term importance engine that applies a query term importance model for advertisement prediction that uses a plurality of term importance weights as a plurality of query features and a plurality of inverse document frequency weights of advertisement terms as a plurality of advertisement features to assign a relevance score to a plurality of sponsored advertisements;

a sponsored advertisement selection engine operably coupled to the query term importance engine that selects the plurality of sponsored advertisements scored by the query term importance engine that applies the query term importance model for advertisement prediction; and

a storage operably coupled to the sponsored advertisement selection engine that stores the query term importance model for advertisement prediction that uses the plurality of term importance weights as the plurality of query features and the plurality of inverse document frequency weights of advertisement terms as advertisement features to assign the relevance score to each of the plurality of sponsored advertisements.

2. The system of claim 1 wherein the storage comprises an advertisement server storage that stores the query term importance model.

3. The system of claim 1 further comprising an advertisement serving engine operably coupled to the sponsored advertisement selection engine that serves at least one of the plurality of sponsored advertisements assigned the relevance score by the query term importance model for advertisement prediction.

4. The system of claim 3 further comprising a web browser operably coupled to the advertisement serving engine that displays the at least one of the plurality of sponsored advertisements in a sponsored advertisement area of a search results web page.

5. A computer-implemented method for predicting relevant search advertisements, comprising:

assigning at least one term importance weight from a query term importance model as at least one query feature to a query;

receiving a plurality of sponsored advertisements with inverse document frequency weights assigned as features to a plurality of terms for each sponsored advertisement;

applying a term importance model for advertisement prediction that uses the at least one term importance weight term as the at least one query feature and a plurality of inverse document frequency weights of advertisement terms as advertisement features to assign a relevance score to each of the plurality of sponsored advertisements;

assigning at least one sponsored advertisement of the plurality of sponsored advertisements assigned the relevance score to at least one web page placement in the sponsored advertisements area of the search results web page; and

sending the at least one sponsored advertisement for display on the search results web page in a location of the at least one web page placement in the sponsored advertisement area of the search results web page.

6. The method of claim 5 further comprising receiving a request to serve the at least one sponsored advertisement for display in the sponsored advertisement area of the search results web page.

7. The method of claim 5 further comprising storing the at least one sponsored advertisement for display on the search results web page in the location of the at least one web page placement in the sponsored advertisement area of the search results web page.

8. The method of claim 5 further comprising assigning the relevance score to each of the plurality of sponsored advertisements.

9. The method of claim 8 further comprising ranking the plurality of sponsored advertisements by the relevance score assigned to each of the plurality of sponsored advertisements.

10. The method of claim 5 further comprising receiving by a client device the at least one sponsored advertisement for display on the search results web page in the location of the at least one web page placement in the sponsored advertisement area of the search results web page.

11. The method of claim 5 further comprising displaying by a client device the at least one sponsored advertisement in the location of the at least one web page placement in the sponsored advertisement area of the search results web page.

12. A computer-readable storage medium having computer-executable instructions for performing the steps of:

receiving a plurality of training sets of a training query and a training advertisement with a training relevance score;

receiving a plurality of term importance weights for each training query in the plurality of training sets of the training query and the training advertisement with the training relevance score;

assigning the plurality of term importance weights as a plurality of training query features to each training query in the plurality of training sets of the training query and the training advertisement with the training relevance score;

training a model that uses the plurality of term importance weights as the plurality of training query features and a plurality of inverse document frequency weights of advertisement terms as training advertisement features for each of the plurality of training sets of the training query and the training advertisement to assign a prediction relevance score to each of the plurality of training sets of the training query and the training advertisement; and

outputting the model to assign the prediction relevance score to a plurality of sets of a query and an advertisement using the plurality of term importance weights as a plurality of query features and the plurality of inverse document frequency weights of advertisement terms as advertisement features for each of the plurality of sets of the query and the advertisement.

13. The method of claim 12 further comprising receiving a plurality of similarity measures for each training set of the plurality of training sets of the training query and the training advertisement with the training relevance score, each similarity measure of the plurality of similarity measures calculated as a cosine similarity measure between the plurality of term importance weights as the plurality of training query features and a plurality of inverse document frequency weights of advertisement terms as training advertisement features for each of the plurality of training sets of the training query and the training advertisement.

14. The method of claim 13 further comprising using the plurality of similarity measures for each training set of the plurality of training sets of the training query and the training advertisement with the training relevance score as a plurality of additional features to train the model that uses the plurality of term importance weights as the plurality of training query features and the plurality of inverse document frequency weights of advertisement terms as training advertisement features for each of the plurality of training sets of the training query and the training advertisement to assign the prediction relevance score to each of the plurality of training sets of the training query and the training advertisement.

15. The method of claim 12 further comprising:

receiving a plurality of n-gram features for each training set of the plurality of training sets of the training query and the training advertisement with the training relevance score; and

using the plurality of n-gram features for each training set of the plurality of training sets of the training query and the training advertisement with the training relevance score as a plurality of additional features to train the model that uses the plurality of term importance weights as the plurality of training query features and the plurality of inverse document frequency weights of advertisement terms as training advertisement features for each of the plurality of training sets of the training query and the training advertisement to assign the prediction relevance score to each of the plurality of training sets of the training query and the training advertisement.

16. The method of claim 12 further comprising:

receiving a plurality of string overlap features for each training set of the plurality of training sets of the training query and the training advertisement with the training relevance score; and

using the plurality of string overlap features for each training set of the plurality of training sets of the training query and the training advertisement with the training relevance score as a plurality of additional features to train the model that uses the plurality of term importance weights as the plurality of training query features and the plurality of inverse document frequency weights of advertisement terms as training advertisement features for each of the plurality of training sets of the training query and the training advertisement to assign the prediction relevance score to each of the plurality of training sets of the training query and the training advertisement.

17. The method of claim 12 further comprising:

receiving a plurality of term translation features for each training set of the plurality of training sets of the training query and the training advertisement with the training relevance score; and

using the plurality of term translation features for each training set of the plurality of training sets of the training query and the training advertisement with the training relevance score as a plurality of additional features to train the model that uses the plurality of term importance weights as the plurality of training query features and the plurality of inverse document frequency weights of advertisement terms as training advertisement features for each of the plurality of training sets of the training query and the training advertisement to assign the prediction relevance score to each of the plurality of training sets of the training query and the training advertisement.

18. The method of claim 13 wherein each similarity measure of the plurality of similarity measures calculated as the cosine similarity measure between the plurality of term importance weights as the plurality of training query features and the plurality of inverse document frequency weights of advertisement terms as training advertisement features for each of the plurality of training sets of the training query and the training advertisement comprises in part a cosine similarity measure calculated between the plurality of term importance weights as the plurality of training query features and a plurality of inverse document frequency weights of advertisement terms from an abstract of the training advertisement as training advertisement features for each of the plurality of training sets of the training query and the training advertisement.

19. The method of claim 13 wherein each similarity measure of the plurality of similarity measures calculated as the cosine similarity measure between the plurality of term importance weights as the plurality of training query features and the plurality of inverse document frequency weights of advertisement terms as training advertisement features for each of the plurality of training sets of the training query and the training advertisement comprises in part a cosine similarity measure calculated between the plurality of term importance weights as the plurality of training query features and a plurality of inverse document frequency weights of advertisement terms from a display uniform resource locator of the training advertisement as training advertisement features for each of the plurality of training sets of the training query and the training advertisement.

20. The method of claim 13 wherein each similarity measure of the plurality of similarity measures calculated as the cosine similarity measure between the plurality of term importance weights as the plurality of training query features and the plurality of inverse document frequency weights of advertisement terms as training advertisement features for each of the plurality of training sets of the training query and the training advertisement comprises in part a cosine similarity measure calculated between the plurality of term importance weights as the plurality of training query features and a plurality of inverse document frequency weights of advertisement terms from a title of the training advertisement as training advertisement features for each of the plurality of training sets of the training query and the training advertisement.