Ranking online advertisement using product and seller reputation

• US 20080288481A1
• 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:
  US11/803,462
• Publication Number:
  US 20080288481A1
• Time in Patent Office: Days
• US Class Current: 707/5
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

Described is a technology by which online advertisements for returning with a query response are ranked according to reputation. The reputation may correspond to a product or service and/or seller reputation. In one example, a set of relevant advertisement items are located and ranked using reputation data as a factor. For example, for each item, a ranking value is based on a mathematical combination of a product reputation score, a seller reputation score and a relevance score, with the items ranked by their computed values. The scores may be weighted differently. The reputation data may be mined from a review source, such as customer reviews available on the web. In one example implementation, a 3-gram model that considers terms in the review along with the two terms proceeding each term is used to analyze the reviews to determine whether each review is positive or negative with respect to the reputation.

Claims

1. In a computing environment, a method comprising:

processing a query;

ranking a set of information comprising a plurality of query-relevant content corresponding to advertisements based on product or service reputation or seller reputation, or a combination of product or service reputation and seller reputation; and

providing at least part of the set as ranked advertisement data based on the ranking, for including in a response to the query.

2. The method of claim 1 wherein processing the query includes performing a relevance ranking to obtain the set of information.

3. The method of claim 2 wherein the relevance ranking includes an advertiser payment factor.

4. The method of claim 1 wherein raking the set of information comprises, for each item of information corresponding to an advertisement, determining a value based on a mathematical combination of a product or service reputation score, a seller reputation score and a relevance score.

5. The method of claim 4 wherein at least two of the scores are weighted differently relative to one another in the mathematical combination.

6. The method of claim 1 further comprising, determining the product or service reputation based on data mined from a review source.

7. The method of claim 6 wherein the data mined from the review source comprises a product or service review, and wherein determining the product or service reputation comprises analyzing text of the product or service review using a model in which a series of terms in the product or service review are analyzed against data in the model to determine whether the review is more likely positive or more likely negative with respect to the product or service reputation.

8. The method of claim 7 wherein the model comprises a 3-gram model, and wherein analyzing the text comprises considering a term and two terms proceeding that term.

9. The method of claim 1 further comprising, determining the seller reputation based on mining data from a review source.

10. The method of claim 9 wherein the data mined from the review source comprises a seller review, and wherein determining the seller reputation comprises analyzing text of the seller review using a model in which a series of terms in the seller,review are analyzed against data in the model to determine whether the review is more likely positive or more likely negative with respect to the seller reputation.

11. The method of claim 10 wherein the model comprises a 3-gram model, and wherein analyzing the text comprises considering a term and two terms proceeding that term.

12. In a computing environment, a system comprising:

means for receiving a query and locating items of data corresponding to advertisements for product or services relevant to that query;

a reputation ranking mechanism that ranks the items of data based on product or service reputation or seller reputation, or a combination of product or service reputation and seller reputation; and

means for providing the items of data for returning as corresponding reputation-ranked advertisement data included in a response to the query.

13. The system of claim 12 wherein the means for receiving the query and locating the items of data includes a relevance ranking mechanism, a payment ranking mechanism, or a combination of a relevance ranking mechanism and a payment ranking mechanism.

14. The system of claim 12 wherein the reputation ranking mechanism is coupled to a source of reputation data.

15. The system of claim 14 wherein the source of reputation data comprises web-available reviews, or a source of reputation data corresponding to web-available reviews, or a combination of web-available reviews and a source of reputation data corresponding to web-available reviews.

16. The system of claim 15 further comprising an analyzer that analyzes text within the web-available reviews using a model to predict whether a review is positive or negative.

17. The system of claim 16 wherein the model comprises a 3-gram model that considers a term and two terms preceding that term.

18. A computer-readable medium having computer-executable instructions, comprising:

accessing a set of data items, each data item corresponding to an advertisement; and

ranking at least part of the set of data items based on a combination of reputation data and relevance to a query or advertiser payment, or a combination of reputation data and both relevance to a query and advertiser payment.

19. The computer-readable medium of claim 18 wherein ranking the data items includes determining a value based for each item based on a mathematical combination of a product or service reputation score, a seller reputation score and a relevance score, and re-ranking according to the score determined for each item.

20. The computer-readable medium of claim 18 wherein the reputation data is determined from web-available reviews by analyzing text in the reviews using a 3-gram model that considers terms and two terms preceding each of those terms in the text.