LOCAL SEARCH USING FEATURE BACKOFF
First Claim
1. A computer-implemented method for perform a search of local entities using supplemental location-specific information, the method comprising:
- receiving a search query from a user searching for one or more local entities;
performing a general search that identifies a body of matching results;
pre-filtering the identified results to eliminate irrelevant search results;
acquiring one or more features of supplemental information related to location that provide one or more hints describing relevance of individual search results;
smoothing one or more features of the acquired supplemental information to handle data sparseness and anomalies;
ranking the search results based on the smoothed features of the acquired supplemental data; and
outputting the ranked results to the user,wherein the preceding steps are performed by at least one processor.
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Abstract
A local search system is described herein that provides a framework for the integration of various external sources to improve local search ranking. The framework provided by the local search system described herein uses a notion of backoff. The system uses a generalization of the concept of backoff to improve local search results that incorporate a variety of data features. The system can apply backoff in multiple dimensions at the same time to generate features for local search ranking. The system integrates various additional data sources, such as web access logs, driving direction request logs, reviews, and so forth, to quantify popularity and distance (or distance sensitivity) into a framework for local search ranking. Thus, the system provides search results that are more relevant by incorporating a number of data sources into the ranking in a manner that handles abnormalities in the data well.
35 Citations
20 Claims
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1. A computer-implemented method for perform a search of local entities using supplemental location-specific information, the method comprising:
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receiving a search query from a user searching for one or more local entities; performing a general search that identifies a body of matching results; pre-filtering the identified results to eliminate irrelevant search results; acquiring one or more features of supplemental information related to location that provide one or more hints describing relevance of individual search results; smoothing one or more features of the acquired supplemental information to handle data sparseness and anomalies; ranking the search results based on the smoothed features of the acquired supplemental data; and outputting the ranked results to the user, wherein the preceding steps are performed by at least one processor. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A computer system for performing local search using feature backoff, the system comprising:
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a processor and memory configured to execute software instructions embodied within the following components; a query receiving component that receives a query from a user that requests a search for local businesses; a search component that performs a search based on the query using a pre-built search index that classifies a set of content; a data acquisition component that acquires supplemental information for ranking multiple identified search results from one or more external data sources; a data backoff component that applies one or more backoff criteria to acquired supplemental information to manage errors or sparseness in the acquired data; and a result ranking component that ranks search results according to the applied backoff criteria and acquired supplemental information. - View Dependent Claims (14, 15, 16, 17, 18, 19)
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20. A computer-readable storage medium comprising instructions for controlling a computer system to smooth potentially unreliable supplemental information with backoff, wherein the instructions, upon execution, cause a processor to perform actions comprising:
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receiving one or more dimensions of supplemental information for ranking results of a local search query designed to identify one or more local entities related to a search query; selecting at least one received dimension; retrieving dimension data related to the selected dimension; determining a reliability measure of the retrieved dimension data; applying backoff to identify related dimension data that fills any gaps in data for the selected dimension; aggregating data for each dimension to create a score for each search result; and applying the aggregated dimension data to rank search results.
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Specification