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US 8,898,095 B2Granted
Systems and methods to facilitate local searches via location disambiguation
AssigneeAT&T Intellectual Property I, L.P.
InventorsRitesh Jitendra Agrawal, James G. Shanahan
Life of this patent
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Abstract
Systems and methods use machine learning techniques to resolve location ambiguity in search queries. In one aspect, a dataset generator generates a training dataset using query logs of a search engine. A training engine applies a machine learning technique to the training dataset to generate a location disambiguation model. A location disambiguation engine uses the location disambiguation model to resolve location ambiguity in subsequent search queries.
Claims16
A method, comprising:
- receiving, at a computing device from a searcher, a search query having location ambiguity;
- generating, by the computing device, a location model using a machine learning technique, the location model generated, at least in part, based on a personalization feature, wherein the personalization feature comprises a location corresponding to a past result selected by the searcher, wherein generating the location model based, at least in part, on the personalization feature comprises generating a training dataset comprising the personalization feature, wherein generating the training dataset comprises
- filtering a query log to identify a first query, the first query specifying an unambiguous location,
- generating, from the first query, a second query having location ambiguity by removing a first component of the first query, wherein removing the first component comprises
- breaking the first query into the first component and a second component, and
- removing the first component,
- identifying a location candidate for the second query, and
- generating a training target based on comparing the unambiguous location specified in the first query and the location candidate for the second query;
- identifying, by the computing device using the location model, an unambiguous location for the search query to resolve the location ambiguity;
- and
- determining, by the computing device, a search result corresponding to the search query directed to the unambiguous location for the search query.
8 more elements
A non-transitory computer readable medium storing instructions which, when executed by a computer comprising a processor, cause the computer to perform operations comprising:
- receiving a search query having location ambiguity;
- generating a location model using a machine learning technique, the location model generated, at least in part, based on a personalization feature, wherein the personalization feature comprises a location corresponding to a past result selected by a searcher, wherein generating the location model based, at least in part, on the personalization feature comprises generating a training dataset comprising the personalization feature, wherein generating the training dataset comprises
- filtering a query log to identify a first query, the first query specifying an unambiguous location,
- generating, from the first query, a second query having location ambiguity by removing a first component of the first query, wherein removing the first component comprises
- breaking the first query into the first component and a second component, and
- removing the first component,
- identifying a location candidate for the second query, and
- generating a training target based on comparing the unambiguous location specified in the first query and the location candidate for the second query;
- identifying, using the location model, an unambiguous location for the search query to resolve the location ambiguity;
- and
- determining a search result corresponding to the search query directed to the unambiguous location for the search query.
8 more elements
A system, comprising:
- a processor;
- and
- a memory that stores instructions that, when executed by the processor, cause the processor to perform operations comprising
- receiving a search query having location ambiguity,
- generating a location model using a machine learning technique, the location model generated, at least in part, based on a personalization feature, wherein the personalization feature comprises a location corresponding to a past result selected by a searcher, wherein generating the location model based, at least in part, on the personalization feature comprises generating a training dataset comprising the personalization feature, wherein generating the training dataset comprises
- filtering a query log to identify a first query, the first query specifying an unambiguous location,
- generating, from the first query, a second query having location ambiguity by removing a first component of the first query, wherein removing the first component comprises
- breaking the first query into the first component and a second component, and
- removing the first component,
- identifying a location candidate for the second query, and
- generating a training target based on comparing the unambiguous location specified in the first query and the location candidate for the second query,
- identifying, using the location model, an unambiguous location for the search query to resolve the location ambiguity, and
- determining a search result corresponding to the search query directed to the unambiguous location for the search query.
10 more elements
Dependencies are inferred from the claim text.