Personalize Search Results for Search Queries with General Implicit Local Intent

• US 20110184981A1
• Filed: 01/27/2010
• Published: 07/28/2011
• Est. Priority Date: 01/27/2010
• Status: Abandoned Application

• Current Assignee: Oath Inc. (Verizon Communications Inc.)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Peng, Fuchun, Dumoulin, Benoit, Lu, Yumao
• Application Number:
  US12/694,515
• Publication Number:
  US 20110184981A1
• Time in Patent Office: Days
• US Class Current: 707/774
• CPC Class Codes: G06F 16/9537 Spatial or temporal dependent retrieval, e.g. spatiotemporal queries

Abstract

One particular embodiment accesses a first set of search queries comprising one or more first search queries; extracts one or more features based on the first set of search queries, trains a search-query classifier using the features; accesses a second search query provided by a user; determines whether the second search query has implicit and general local intent using the search-query classifier; if the second search query has implicit and general local intent, then determines a location associated with the user; and identifies a search result in response to the second search query based at least in part on the location associated with the user; and presents the search result to the user.

Claims

1. A method comprising, by one or more computer systems:

accessing a first set of search queries comprising one or more first search queries;

extracting one or more features based on the first set of search queries, the features comprising one or more of;

one or more first features indicating, for each of the first search queries, whether the first search query has local intent;

one or more second features indicating, for each of the first search queries that have local intent, whether the local intent is implicit; and

one or more third features indicating, for each of the first search queries that have local intent, whether the local intent is general;

training a search-query classifier using the features;

accessing a second search query provided by a user;

determining whether the second search query has implicit and general local intent using the search-query classifier;

if the second search query has implicit and general local intent, then;

determining a location associated with the user; and

identifying a search result in response to the second search query based at least in part on the location associated with the user; and

presenting the search result to the user.

2. The method recited in claim 1, wherein the features further comprise one or more of:

one or more fourth features indicating a frequency of person names among the first search queries; and

one or more fifth features indicating a weight of domain names among the first search queries.

3. The method recited in claim 1, wherein extracting the features based on the first set of search queries comprises:

constructing a second set of search queries comprising one or more of the first search queries in the first set of search queries, wherein each of the first search queries in the second set of search queries comprises one or more words that represent a location;

for each of the first search queries in the second set of search queries, removing the words that represent the location to obtain a modified first search query;

constructing a third set of search queries comprising the modified first search queries;

constructing a first language model based on the first set of search queries;

constructing a second language model based on the second set of search queries;

constructing a third language model based on the third set of search queries; and

extracting the features based on the first language model, the second language model, and the third language model.

4. The method recited in claim 1, wherein:

the search-query classifier is a non-linear support vector machine (SVM) classifier that, given a search query, predicts a probability that the search query has implicit and general local intent; and

the search query has implicit and general local intent if the probability satisfies a predetermined threshold requirement.

5. The method recited in claim 1, wherein if the second search query has implicit and general local intent, then identifying the search result in response to the second search query and based at least in part on the location associated with the user comprises:

constructing a third search query comprising the second search query and the location associated with the user; and

identifying the search result in response to the third search query.

6. The method recited in claim 1, wherein if the second search query has implicit and general local intent, then identifying the search result in response to the second search query and based at least in part on the location associated with the user comprises:

identifying a plurality of network resources in response to the second search query;

ranking the network resources based at least in part on the location associated with the user; and

constructing the search result comprising the ranked network resources.

7. The method recited in claim 1, further comprising if the second search query does not have implicit and general local intent, then identifying the search result in response to the second search query.

8. A system, comprising:

a memory comprising instructions executable by one or more processors; and

one or more processors coupled to the memory and operable to execute the instructions, the one or more processors being operable when executing the instructions to;

access a first set of search queries comprising one or more first search queries;

extract one or more features based on the first set of search queries, the features comprising one or more of;

one or more first features indicating, for each of the first search queries, whether the first search query has local intent;

one or more second features indicating, for each of the first search queries that have local intent, whether the local intent is implicit; and

one or more third features indicating, for each of the first search queries that have local intent, whether the local intent is general;

train a search-query classifier using the features;

access a second search query provided by a user;

determine whether the second search query has implicit and general local intent using the search-query classifier;

if the second search query has implicit and general local intent, then;

determine a location associated with the user; and

identify a search result in response to the second search query based at least in part on the location associated with the user; and

present the search result to the user.

9. The system recited in claim 8, wherein the features further comprise one or more of:

one or more fourth features indicating a frequency of person names among the first search queries; and

one or more fifth features indicating a weight of domain names among the first search queries.

10. The system recited in claim 8, wherein to extract the features based on the first set of search queries comprises:

construct a second set of search queries comprising one or more of the first search queries in the first set of search queries, wherein each of the first search queries in the second set of search queries comprises one or more words that represent a location;

for each of the first search queries in the second set of search queries, remove the words that represent the location to obtain a modified first search query;

construct a third set of search queries comprising the modified first search queries;

construct a first language model based on the first set of search queries;

construct a second language model based on the second set of search queries;

construct a third language model based on the third set of search queries; and

extract the features based on the first language model, the second language model, and the third language model.

11. The system recited in claim 8, wherein:

the search-query classifier is a non-linear support vector machine (SVM) classifier that, given a search query, predicts a probability that the search query has implicit and general local intent; and

the search query has implicit and general local intent if the probability satisfies a predetermined threshold requirement.

12. The system recited in claim 8, wherein if the second search query has implicit and general local intent, then identifying the search result in response to the second search query and based at least in part on the location associated with the user comprises:

construct a third search query comprising the second search query and the location associated with the user; and

identify the search result in response to the third search query.

13. The system recited in claim 8, wherein if the second search query has implicit and general local intent, then identifying the search result in response to the second search query and based at least in part on the location associated with the user comprises:

identify a plurality of network resources in response to the second search query;

rank the network resources based at least in part on the location associated with the user; and

construct the search result comprising the ranked network resources.

14. The system recited in claim 8, wherein the processors are further operable when executing the instructions to, if the second search query does not have implicit and general local intent, then identify the search result in response to the second search query.

15. One or more computer-readable storage media embodying software operable when executed by one or more computer systems to:

access a first set of search queries comprising one or more first search queries;

extract one or more features based on the first set of search queries, the features comprising one or more of;

one or more first features indicating, for each of the first search queries, whether the first search query has local intent;

one or more second features indicating, for each of the first search queries that have local intent, whether the local intent is implicit; and

one or more third features indicating, for each of the first search queries that have local intent, whether the local intent is general;

train a search-query classifier using the features;

access a second search query provided by a user;

determine whether the second search query has implicit and general local intent using the search-query classifier;

if the second search query has implicit and general local intent, then;

determine a location associated with the user; and

identify a search result in response to the second search query based at least in part on the location associated with the user; and

present the search result to the user.

16. The media recited in claim 15, wherein the features further comprise one or more of:

one or more fourth features indicating a frequency of person names among the first search queries; and

one or more fifth features indicating a weight of domain names among the first search queries.

17. The media recited in claim 15, wherein to extract the features based on the first set of search queries comprises:

construct a second set of search queries comprising one or more of the first search queries in the first set of search queries, wherein each of the first search queries in the second set of search queries comprises one or more words that represent a location;

for each of the first search queries in the second set of search queries, remove the words that represent the location to obtain a modified first search query;

construct a third set of search queries comprising the modified first search queries;

construct a first language model based on the first set of search queries;

construct a second language model based on the second set of search queries;

construct a third language model based on the third set of search queries; and

extract the features based on the first language model, the second language model, and the third language model.

18. The media recited in claim 15, wherein:

the search-query classifier is a non-linear support vector machine (SVM) classifier that, given a search query, predicts a probability that the search query has implicit and general local intent; and

the search query has implicit and general local intent if the probability satisfies a predetermined threshold requirement.

19. The media recited in claim 15, wherein if the second search query has implicit and general local intent, then identifying the search result in response to the second search query and based at least in part on the location associated with the user comprises:

construct a third search query comprising the second search query and the location associated with the user; and

identify the search result in response to the third search query.

20. The media recited in claim 15, wherein if the second search query has implicit and general local intent, then identifying the search result in response to the second search query and based at least in part on the location associated with the user comprises:

identify a plurality of network resources in response to the second search query;

rank the network resources based at least in part on the location associated with the user; and

construct the search result comprising the ranked network resources.

21. The media recited in claim 15, wherein the software is further operable when executed by the computer systems to, if the second search query does not have implicit and general local intent, then identify the search result in response to the second search query.