Ranking online advertisements using retailer and product reputations

• US 20080288348A1
• Filed: 05/15/2007
• Published: 11/20/2008
• Est. Priority Date: 05/15/2007
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Chen, Zheng, Zhang, Benyu, Wang, Jian, Lin, Chenxi, Zeng, Huajun, Han, Dingyi
• Application Number:
  US11/803,461
• Publication Number:
  US 20080288348A1
• Time in Patent Office: Days
• US Class Current: 705/14
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0256 User search
• CPC Class Codes: G06Q 30/0263 based upon Internet or website rating
• CPC Class Codes: G06Q 30/0277 Online advertisement

Abstract

A method for ranking online advertisements using retailer reputation and product reputation. In one implementation, a query may be received. Advertisements may be selected by determining a level of relevance between the query and each advertisement and selecting the advertisements with a level of relevance above a pre-determined level of relevance. A predicted reputation for a retailer and a predicted reputation for a product may be retrieved for each of the selected advertisements. The selected advertisements may then be ranked based on the predicted reputation for the retailer and the predicted reputation of the product. The ranking of the selected advertisements may be accomplished by calculating a ranking score for each selected advertisement based on the retailer predicted reputation and the product predicted reputation. The selected advertisements may then be displayed according to the ranking.

Claims

1. A method for ranking online advertisements using retailer reputation and product reputation, comprising:

receiving a query;

selecting one or more advertisements based on the query;

retrieving a predicted reputation for a retailer and a predicted reputation for a product associated with each selected advertisement; and

ranking the selected advertisements based on the predicted reputation for the retailer and the predicted reputation of the product.

2. The method of claim 1, wherein ranking the selected advertisements comprises calculating a ranking score for each selected advertisement based on the retailer predicted reputation and the product predicted reputation.

3. The method of claim 2, wherein calculating the ranking score comprises summing weighted factors of the retailer predicted reputation, the product predicted reputation, relevance and other optional factors.

4. The method of claim 1, wherein ranking the selected advertisements comprises calculating a ranking score for each selected advertisement based on the retailer predicted reputation, the product -predicted reputation, relevance and other optional factors.

5. The method of claim 1, wherein the advertisements are selected from an advertisement database.

6. The method of claim 1, wherein retrieving the predicted reputation for the retailer and the predicted reputation for the product comprises retrieving a predicted reputation for each retailer and product associated with each selected advertisement.

7. The method of claim 1, further comprising displaying the ranked advertisements.

8. The method of claim 1, wherein selecting the advertisements comprises:

determining a level of relevance between the query and each advertisement; and

selecting the advertisements having a level of relevance above a pre-determined level of relevance.

9. The method of claim 1, wherein the reputation for the retailer or the reputation for the product associated with each selected advertisement or both is predicted by:

collecting one or more online reviews of the retailer or the product or both that are associated with each selected advertisement;

determining a probability of a positive orientation and a probability of a negative orientation for each online review;

determining an orientation for each online review by comparing the probability of the positive orientation with the probability of the negative orientation; and

calculating the predicted reputation of the retailer or the product or both based on a percentage of online reviews with a positive orientation.

10. The method of claim 9, wherein determining the probability of the positive orientation and the probability of the negative orientation comprises:

determining the probability of the positive orientation for each online review by comparing each online review to a positive review trigram model; and

determining the probability of the negative orientation for each online review by comparing each online review to a negative review trigram model.

11. The method of claim 9, wherein determining the probability of the positive orientation for each online review comprises:

determining one or more trigram phrases in each online review that match one or more trigram phrases in the positive review trigram model;

retrieving a probability for each matching trigram phrase; and

multiplying one or more probabilities for the matching trigram phrases to determine the probability of the positive orientation.

12. The method of claim 9, wherein determining the probability of the negative orientation for each online review comprises:

determining one or more trigram phrases in each online review that match one or more trigram phrases in the negative review trigram model;

retrieving a probability for each matching trigram phrase; and

multiplying one or more probabilities for the matching trigram phrases to determine the probability of the negative orientation.

13. A method for predicting a reputation for a retailer or a product or both, comprising:

collecting one or more online reviews of the retailer or the product or both that are associated with an advertisement;

determining a probability of a positive orientation and a probability of a negative orientation for each online review;

determining an orientation for each online review by comparing the probability of the positive orientation with the probability of the negative orientation; and

calculating the predicted reputation of the retailer or the product or both based on a percentage of online reviews with a positive orientation.

14. The method of claim 13, wherein determining the probability of the positive orientation and the probability of the negative orientation comprises:

determining the probability of the positive orientation for each online review by comparing each online review to a positive review trigram model; and

determining the probability of the negative orientation for each online review by comparing each online review to a negative review trigram model.

15. The method of claim 14, wherein determining the probability of the positive orientation for each online review comprises:

determining one or more trigram phrases in each online review that match one or more trigram phrases in the positive review trigram model;

retrieving a probability for each matching trigram phrase; and

multiplying one or more probabilities for the matching trigram phrases to determine the probability of the positive orientation.

16. The method of claim 14, wherein determining the probability of the negative orientation for each online review comprises:

determining one or more trigram phrases in each online review that match one or more trigram phrases in the negative review trigram model;

retrieving a probability for each matching trigram phrase; and

multiplying one or more probabilities for the matching trigram phrases to determine the probability of the negative orientation.

17. The method of claim 14, wherein the positive review trigram model is developed by:

collecting one or more online reviews for a retailer or product or both as training reviews;

determining an orientation of each training review; and

calculating a probability for each trigram phrase in the training reviews having a positive orientation.

18. The method of claim 14, wherein the negative review trigram model is developed by:

collecting one or more online reviews for a retailer or product or both as training reviews;

determining an orientation of each training review; and

calculating a probability for each trigram phrase in the training reviews having a negative orientation.

19. A computer system, comprising:

a processor; and

a memory comprising program instructions executable by the processor to;

receive a query;

determine a level of relevance between the query and one or more advertisements in a set of advertisements;

select the advertisements having a level of relevance above a pre-determined level of relevance;

retrieve a predicted reputation for a retailer and a predicted reputation for a product associated with each selected advertisement;

calculate a ranking score for each selected advertisement based on the predicted reputation for the retailer and the predicted reputation for the product; and

rank the selected advertisements based on the ranking score.

20. The computer system of claim 19, wherein the memory further comprises program instructions executable by the processor to display the ranked advertisements.