SYSTEM OF RANKING SEARCH RESULTS BASED ON QUERY SPECIFIC POSITION BIAS

• US 20100153370A1
• Filed: 12/15/2008
• Published: 06/17/2010
• Est. Priority Date: 12/15/2008
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Gollapudi, Sreenivas, Panigrahy, Rina
• Application Number:
  US12/335,396
• Publication Number:
  US 20100153370A1
• Time in Patent Office: Days
• US Class Current: 707/722
• CPC Class Codes: G06F 16/958 Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking

Abstract

A model based on a generalization of the Examination Hypothesis is disclosed that states that for a given query, the user click probability on a document in a given position is proportional to the relevance of the document and a query specific position bias. Based on this model the relevance and position bias parameters are learned for different queries and documents. This is done by translating the model into a system of linear equations that can be solved to obtain the best fit relevance and position bias values. A cumulative analysis of the position bias curves may be performed for different queries to understand the nature of these curves for navigational and informational queries. In particular, the position bias parameter values may be computed for a large number of queries. Such an exercise reveals whether the query is informational or navigational. A method is also proposed to solve the problem of dealing with sparse click data by inferring the goodness of unclicked documents for a given query from the clicks associated with similar queries.

Claims

1. A method for transforming search results for a search performed by a search engine, the method comprising the steps of:

(a) logging a search query, search results in a ranked position order and click through counts for the search results in a storage location;

(b) determining a goodness value for each stored search result for the query, the goodness value for each search result representing a relevance of the search result to the query;

(c) determining a position bias for each search result position for the query based in part on the particular query;

(d) transforming the search results by reordering the ranked position of the results based on a probability that a particular search result will be clicked on, the probability based on a product of the goodness value determined in said step (b) and the position bias determined in said step (c); and

(e) displaying the search results in the reordered ranked positions determined in said step (d) upon a next entry of the query.

2. The method of claim 1, wherein said step (b) of determining a goodness value for a search result comprises the step of determining a probability that the search result will be clicked on if positioned in the highest ranked position.

3. The method of claim 2, wherein said step of determining a probability that the search result will be clicked on if positioned in the highest ranked position comprises the step of examining all stored instances of the query and that search result.

4. The method of claim 1, wherein said step (c) of determining a position bias for a search result position comprises the step of determining a ratio of the probability that a search result at a given ranked position is clicked to the probability of that search result being clicked if positioned in the highest ranked position.

5. The method of claim 4, further comprising the step of determining whether the query is a navigational query or an informational query based on the determined position bias values for search results of the query.

6. The method of claim 1, wherein the determinations made in said steps (b) and (c) comprise the step of solving for the values of g(d) and p(j) in a system of equations in the form of c(d, j)=g(d)p(j), where c(d, j) is the probability that, for stored instances of the same query, a document d in a position j was clicked, g(d) is the goodness value of a document d, and p(j) is a position bias of a ranked position j.

7. The method of claim 6, wherein the number of variables in the system of equations is the sum of the number of distinct documents d and the sum of distinct positions j logged in the storage location for all instances of the same query, and the number of equations is equal to the number of search results logged in the storage location for all instances of the same query.

8. The method of claim 7, wherein, in the event the system is over-constrained by virtue of the equations outnumbering the variables, the equations are solved by minimizing the solution error using an error minimization norm.

9. The method of claim 1, further comprising the step of inferring the goodness values g(d) of documents which were not clicked on for the query q by considering additional search queries that are related to the search query for which the documents were clicked on.

10. The method of claim 9, wherein an additional search query is related to the search query if the additional search query shares a predetermined number of search results with the search query.

11. A method for transforming search results for a search performed by a search engine, the method comprising the steps of:

(a) logging a search query, search results in a ranked position order and click through counts for the search results in a storage location;

(b) transforming the ranking of the search results for the query by the step of determining a probability, c(d, j), that a search result document d at a position j in the ranked position order for the query will be clicked on by solving a system of equations c(d, j)=g(d)p(j), where g(d) is a goodness value based on a probability that the search result document d will be clicked on if positioned in the highest ranked position for the query, and p(j) is a position bias based on a ratio of the probability that a search result at a given ranked position j is clicked to the probability of that search result being clicked if positioned in the highest ranked position, wherein position bias may vary from query to query, and wherein the system of equations is obtained from the stored instances of the search results for the query; and

(c) displaying the search results in the reordered ranked positions determined in said step (b) upon a next entry of the query.

12. The method of claim 11, wherein the number of variables in the system of equations is the sum of the number of distinct documents d and the sum of distinct positions j logged in the storage location for all instances of the same query, and the number of equations is equal to the number of search results logged in the storage location for all instances of the same query.

13. The method of claim 11, wherein, if modeled on a bipartite graph having the documents d as vertices on a first side, the positions j as vertices on the second side, and edges between a pair of vertices (d, j) representing a search result document d for the query that has appeared in the ranked position order j, the values for g(d) and p(j) may be deduced if all documents are connected to all positions, directly or indirectly, via an edge.

14. The method of claim 11, wherein, if modeled on a bipartite graph having the documents d as vertices on a first side, the positions j as vertices on the second side, and an edge between a pair of vertices (d, j) representing that a search result document d for the query has appeared in the ranked position order j, the values for g(d) and p(j) may be deduced if all documents are connected to all positions, directly or indirectly, via an edge.

15. The method of claim 14, wherein, if the bipartite graph includes one or more disconnected components, the values of g(d) from different components may be compared based on determining a parameterized curve that approximates all position bias curves resulting from the distinct components, estimating a probability that a search result will be clicked based on the parameterized curve and measuring the click through rate for the different positions j, giving equal weight to each document.

16. The method of claim 11, further comprising the step of determining whether the query is a navigational query or an informational query based on the determined position bias values p(j) for search results of the query.

17. A computer storage medium having computer-executable instructions for programming a processor to perform a method of transforming search results for a search performed by a search engine, the method comprising the steps of:

(a) logging a search query, search results in a ranked position order and click through counts for the search results in a storage location;

(b) determining goodness values, g(d), for each stored search result document d for the query, the goodness value for each search result representing a relevance of the search result to the query;

(c) determining a position bias, p(j), for each search result position j for the query based in part on the particular query, position bias for a search result position being a ratio of the probability that a search result at a given ranked position j is clicked to the probability of that search result being clicked if positioned in the highest ranked position, said steps (b) and (c) being performed by solving for the values of g(d) and p(j) using a system of equations in the form of c(d, j)=g(d)p(j), where c(d, j) is the probability that, for stored instances of the same query, a document d in a position j was clicked;

(d) transforming the search results by reordering the ranked position of the results based on a probability that a particular search result will be clicked on based on said step (c); and

(e) displaying the search results in the reordered ranked positions determined in said step (d) upon a next entry of the query.

18. The method of claim 17, further comprising the step of determining whether the query is a navigational query or an informational query based on the determined position bias values p(j) for search results of the query.

19. The method of claim 17, further comprising the step of inferring the goodness values g(d) of documents which were not clicked on for the query q by considering additional search queries that are related to the search query for which the documents were clicked on.

20. The method of claim 19, wherein an additional search query is related to the search query if the additional search query shares a predetermined number of search result documents d with the search query.