TABLE APPROACH FOR DETERMINING QUALITY SCORES

• US 20120253899A1
• Filed: 04/01/2011
• Published: 10/04/2012
• Est. Priority Date: 04/01/2011
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Qin, Tao, Liu, Tie-Yan, Xie, Wei, Gao, Chi, Ma, Wei-Ying, Xu, Zeyong
• Application Number:
  US13/078,329
• Publication Number:
  US 20120253899A1
• Time in Patent Office: Days
• US Class Current: 705/14.4
• CPC Class Codes: G06Q 30/0241 Advertisements

Abstract

Some implementations construct a quality score table based on historic data collected for a plurality of ad-keyword pairs. An ad-keyword pair may be selected for determining a quality score. One or more advertisement parameters may be determined for the selected ad-keyword pair. Based on the one or more advertisement parameters, the quality score for the selected ad-keyword pair may be determined from the quality score table. In some implementations, the quality score table is constructed by iteratively cutting a directed graph representing the advertisement parameters and the historic data. Further, in some implementations, the table may be smoothed using a smoothing operation.

Claims

1. A method comprising:

under control of one or more processors configured with executable instructions,

selecting an ad-keyword pair for determining a quality score;

determining one or more advertisement parameters for the selected ad-keyword pair;

based at least in part on the one or more advertisement parameters, determining the quality score from a quality score table constructed based at least in part on historic data for a plurality of ad-keyword pairs; and

utilizing the quality score in an advertisement service.

2. The method as recited in claim 1, further comprising constructing the quality score table from the historic data, wherein the historic data comprises historic performance data for the plurality of ad-keyword pairs and a plurality of corresponding advertisement parameters for the plurality of ad-keyword pairs.

3. The method as recited in claim 2, wherein the historic performance data serves as a ground truth for constructing the quality score table and comprises at least one of:

click-through rates for the plurality of ad-keyword pairs;

number of impressions for the plurality of ad-keyword pairs;

number of ad clicks for the plurality of ad-keyword pairs;

number of leads for the plurality of ad-keyword pairs;

or

number of sales for the plurality of ad-keyword pairs.

4. The method as recited in claim 2, wherein the advertisement parameters comprise one or more of:

relevance between the keyword and an advertisement of the selected ad-keyword pair;

relevance between the keyword and a landing page for the ad-keyword pair; and

quality of the landing page for the ad-keyword pair.

5. The method as recited in claim 2, wherein constructing the quality score table further comprises constructing a directed graph in which nodes of the graph represent values of ad parameters for the plurality of ad-keyword pairs, and the historic performance data is used to determine weights of edges of the directed graph.

6. The method as recited in claim 5, wherein constructing the quality score table further comprises cutting the directed graph into a plurality of subgraphs, wherein each subgraph represents a quality score value in the quality score table.

7. The method as recited in claim 6, further comprising smoothing the quality score table using a greedy smoothing operation.

8. The method as recited in claim 1, further comprising providing the quality score as feedback to an advertiser that is a source of the selected ad-keyword pair.

9. The method as recited in claim 1, further comprising periodically updating the quality score table as new historic data is collected for the plurality of ad-keyword pairs.

10. A computing device comprising:

one or more processors in operable communication with computer-readable media;

a quality score component executed on the one or more processors to perform operations comprising;

constructing a graph from historic data relating to a plurality of ad-keyword pairs;

cutting the graph iteratively into a plurality of subgraphs;

generating a quality score table containing a plurality of quality score values, each subgraph corresponding to a different quality score value in the quality score table.

11. The computing device as recited in claim 10, wherein the historic data comprises a performance indicator for the plurality of ad-keyword pairs used as a ground truth for constructing the graph.

12. The computing device as recited in claim 11, wherein the performance indicator comprises one of:

click-through rates for the plurality of ad-keyword pairs;

number of impressions for the plurality of ad-keyword pairs;

number of ad clicks for the plurality of ad-keyword pairs;

number of leads for the plurality of ad-keyword pairs;

or

number of sales for the plurality of ad-keyword pairs.

13. The computing device as recited in claim 11, wherein the historic data further comprises advertisement parameters for the plurality of ad-keyword pairs, the advertisement parameters comprising one or more of:

relevance between the keyword and an advertisement of an ad-keyword pair;

relevance between the keyword and a landing page for the ad-keyword pair; and

quality of the landing page for the ad-keyword pair.

14. The computing device as recited in claim 10, wherein the graph comprises:

a plurality of nodes that represent values of advertisement parameters for the plurality of ad-keyword pairs; and

a plurality edges, wherein historic performance data of the plurality of ad-keyword pairs is used to determine weights of the plurality of edges of the graph.

15. The computing device as recited in claim 14, wherein the cutting of the graph into subgraphs includes using the weights of the edges to determine to which subgraph each node belongs.

16. The computing device as recited in claim 10, the operations further comprising smoothing the quality score table using a greedy smoothing operation to provide the quality score table with a monotonic increasing property.

17. One or more computer-readable media having instructions stored thereon executable by a processor to perform operations comprising:

obtaining historic data for a plurality of ad-keyword pairs;

constructing a directed graph from the historic data;

determining weights of edges in the directed graph based at least in part on a ground truth determined from the historic data;

cutting the directed graph into a plurality of subgraphs based at least in part on the weights of the edges; and

generating a quality score table having a plurality of quality score values that are related to the plurality of subgraphs.

18. The one or more computer-readable media as recited in claim 17, the operations further comprising smoothing the quality score table using a greedy smoothing function.

19. The one or more computer-readable media as recited in claim 17, the operations further comprising employing the quality score table to determine quality scores for one or more ad-keyword pairs.

20. The one or more computer-readable media as recited in claim 17, wherein each node in the graph corresponds to a triple representing values of three advertisement parameters.