Biasing queries to determine suggested queries

• US 20070050339A1
• Filed: 05/08/2006
• Published: 03/01/2007
• Est. Priority Date: 08/24/2005
• Status: Active Grant

• Current Assignee: Jollify Management Limited
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Maghoul, Farzin, Kasperski, Richard
• Granted Patent: US 7,844,599 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/9032 Query formulation

Abstract

Applying a bias when determining a suggested search query. Examples of biases that can be applied include, but are not limited to, temporal biases and monetization biases. Temporal biasing involves increasing a weight associated with a search query, based on a temporal attribute associated with the query. Search queries may also have associated with them a parameter such as frequency, count, etc. One of these parameters may serve as a weight. In order to determine suggested search queries, the temporal attribute is used to modify or bias the parameter (e.g., frequency parameter). Thus, the weight of the search query is modified to temporally bias the query. The suggested search queries are determined based on the biased parameter.

Claims

1. A method comprising performing a machine-executed operation involving instructions, wherein the machine-executed operation is at least one of:

A) sending said instructions over transmission media;

B) receiving said instructions over transmission media;

C) storing said instructions onto a machine-readable storage medium; and

D) executing the instructions;

wherein said instructions are instructions which, when executed by one or more processors, cause the one or more processors to perform the steps of;

receiving a user search query from a client node;

generating a set of biased suggested query candidates by biasing, based on an attribute associated with each suggested query candidate, a parameter that is associated with said suggested query candidate;

based on the biased parameters associated with the suggested query candidates, selecting one or more of the suggested query candidates to be suggested queries for the user search query; and

providing at least one of the suggested queries to the client node.

2. The method as recited in claim 1, wherein the generating a set of biased suggested query candidates is performed prior to receiving the user search query.

3. The method as recited in claim 1, wherein the generating a set of biased suggested query candidates is performed after receiving the user search query.

4. The method as recited in claim 1, wherein the parameter that is associated with said suggested query candidate is based on a count of how many times the suggested query candidate was submitted.

5. The method as recited in claim 1, wherein the attribute is a temporal indication that reflects when the suggested query candidate was previously received.

6. The method as recited in claim 1, wherein the attribute is a monetization value that reflects a monetary value that is associated with the suggested query candidate.

7. The method of claim 1, further comprising the step of determining the suggested query candidates based, at least in part, on a count of how many times the suggested query candidates have been received from users.

8. The method of claim 1, wherein the suggested queries comprise at least one of:

a) predicted query completions;

b) alternative search queries;

c) alternative spelling suggestions; and

d) also try query suggestions.

9. A method comprising performing a machine-executed operation involving instructions, wherein the machine-executed operation is at least one of:

A) sending said instructions over transmission media;

B) receiving said instructions over transmission media;

C) storing said instructions onto a machine-readable storage medium; and

D) executing the instructions;

wherein said instructions are instructions which, when executed by one or more processors, cause the one or more processors to perform the steps of;

prior to receiving a user query, generating a biased parameter for each of a plurality of suggested query candidates, wherein the biased parameter for each suggested query candidate is generated by biasing a parameter associated with the suggested query based on an attribute associated with the suggested query candidate;

receiving the user search query from a client node;

based, at least in part, on the biased parameters associated with the suggested query candidates, selecting one or more of the suggested query candidates to be suggested queries for the user search query; and

providing at least one of the suggested queries to the client node.

10. The method as recited in claim 9, wherein the parameter that is associated with said suggested query is based a count of how many times the suggested query was submitted.

11. The method as recited in claim 9, wherein the attribute is temporal indication that reflects when the suggested query candidate was previously received.

12. The method as recited in claim 9, wherein the attribute is monetization value that reflects a monetary value that is associated with the suggested query candidate.

13. The method of claim 9, wherein the suggested queries comprise at least one of:

a) predicted query completions;

b) alternative search queries;

c) alternative spelling suggestions; and

d) also try query suggestions.

14. A method comprising performing a machine-executed operation involving instructions, wherein the machine-executed operation is at least one of:

A) sending said instructions over transmission media;

B) receiving said instructions over transmission media;

C) storing said instructions onto a machine-readable storage medium; and

D) executing the instructions;

wherein said instructions are instructions which, when executed by one or more processors, cause the one or more processors to perform the steps of;

receiving a user search query from a client node;

determining a set of suggested query candidates;

based on a set of one or more factors, selecting one or more of the suggested query candidates to be suggested queries for the user search query;

wherein the set of one or more factors includes at least one of (a) temporal information associated with each of the suggested query candidates and (b) monetization information associated with each of the suggested query candidates; and

providing at least one of the suggested queries to the client node.

15. A method as recited in claim 14, wherein the set of one or more factors includes temporal information associated with each of the suggested query candidates.

16. A method as recited in claim 15, wherein the set of one or more factors includes monetization information associated with each of the suggested query candidates.

17. A method as recited in claim 14, wherein the set of one or more factors includes monetization information associated with each of the suggested query candidates.

18. A method as recited in claim 14, wherein the set of one or more factors includes information associated with a user that submitted the user search query.

19. A method as recited in claim 14, wherein the method further comprises:

prior to receiving the user query, generating a biased parameter for each of the suggested query candidates, wherein the biased parameter for each suggested query candidate is generated by biasing a parameter associated with the suggested query candidate based on one or more of (a) temporal information associated with each of the suggested query candidates and (b) monetization information associated with each of the suggested query candidates; and

wherein selecting one or more of the suggested query candidates to be suggested queries for the user search query is based, at least in part, on the biased parameters associated with the suggested query candidates.

20. The method of claim 14, wherein the step of determining a set of suggested query candidates includes determining a set of suggested query candidates based, at least in part, on a count of how many times suggested query candidates have been received from users.

21. The method of claim 20, wherein the step of determining a set of suggested query candidates includes determining the set of suggested query candidates based further on at least one of (a) temporal information associated with each of the suggested query candidates and (b) monetization information associated with each of the suggested query candidates.

22. The method of claim 14, wherein the suggested queries comprise at least one of:

a) predicted query completions;

b) alternative search queries;

c) alternative spelling suggestions; and

d) also try query suggestions.