Google Inc.

Abhinandan Sujit Das

Anwis Das

Nitin Kumar Gupta

Ashutosh Garg

Mayur Dhondu Datar

Rajat Mukherjee

Renshen Wang

Dan Moisa

Jennifer Liu Low

Harry S Fung

Nitin Gupta

Bartlomiej M Niechwiej

Adam Westall

Othar Hansson

Michael D Levin

Jonathan James Effrat

Marcin K Wichary

James Home

Methods and apparatus for providing query suggestions to a user based on one or more past queries submitted by the user. Candidate query suggestions responsive to a current query may be identified. A candidate query similarity measure may be determined for a given candidate query suggestion based on matching entities related to the given candidate query suggestion and the one or more past queries. In some implementations, the similarity measure of the given candidate query suggestion may be based on a comparison of current entities of the given candidate query suggestion that match entities of one or more past queries, to a group of the current entities that includes entities that do not match the entities of one or more past queries. In some implementations a ranking of the candidate query suggestions may be determined based on the similarity measure.

Query suggestions based on entity collections of one or more past queries

Methods and apparatus for providing query suggestions to a user based on one or more past queries submitted by the user. Candidate query suggestions responsive to a current query may be identified. A candidate query similarity measure may be determined for a given candidate query suggestion based on matching entities related to the given candidate query suggestion and the one or more past queries. In some implementations the similarity measure may be based on cumulative entity scores of the matching entities that may optionally be based on at least one of inverse popularity of the matching entities, and how recent a past query is. In some implementations a ranking of the candidate query suggestions may be adjusted based on the similarity measure. A given candidate query suggestion may be selected as a query suggestion based on at least one of the adjusted ranking and the candidate query similarity measure.

Personalized suggestions based on past queries

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for related search suggestions for products. In one aspect, a method includes receiving a query for searching documents wherein the query specifies a first product. A number of occurrences of the first product in a plurality of prior queries of the documents submitted by users is determined. A product category for the first product is determined based on one or more attributes associated with the first product, the category having a predetermined plurality of category attributes. A suggested product is selected wherein the suggested product is associated with attributes that satisfy the category attributes.

Method, system, and computer readable medium for rendering a graphical user interface visually indicating search results including related suggested products

Methods, systems and apparatus, including computer programs encoded on a computer storage medium, for disambiguating names in a document corpus. In an aspect, a method includes generating context term lists for a person name, each context term list being a list of context terms from a resource for the person name; clustering the context term lists into a plurality of clusters, each of the clusters of context term lists including context term lists that are most similar to the cluster relative to other clusters; for each of the clusters, selecting a representative term for the cluster; receiving the person name as a search query; and generating a plurality of query suggestions from the search query and the representative terms for the clusters, each query suggesting being a combination of the person name and one representative term.

Name disambiguation using context terms

Methods, systems, and apparatus, including computer program products, for generating query refinements using query components. In general, one aspect features a method that includes the acts of receiving a query comprising a plurality of terms; identifying first and second components of the query, wherein each component comprises one or more of the terms of the query and the components do not share a term from the query, and wherein the first component appears before the second component in the query; determining, for each component, a plurality of different respective component refinements; and combining the component refinements to create a plurality of query refinements for the query, including combining a first component refinement for the first component with a second component refinement for the second component to create a query refinement so that the first component refinement appears before the second component refinement in the query refinement.

Generating query refinements using query components

A computer-implemented method for identifying related search queries is performed on a server. The method includes receiving a search query from a user, identifying a set of ranked search results satisfying the search query, and identifying, using historical search query data, at least one last related search query in at least one chain of related search queries that is related to the search query and that includes at least one search result that was selected by users who issued the search query, each respective related search query in the at least one chain of related search queries except for the at least one last related search query in the at least one chain of related search queries violating a search result selection criterion. The method further includes returning the set of ranked search results and the at least one last related search query to the user.

System and method for identifying search results satisfying a search query

Methods, systems, and apparatus, including computer program products, for generating query refinements from user preference data. A group of query pairs are obtained. Each query pair includes a first query and a second query. A quality score is determined for each query pair from user preference data for documents responsive to both the first and the second query. A diversity score is determined for each query pair having a quality score satisfying a quality threshold, the diversity score determined from user preference data for documents responsive to the second, but not the first, query. For each query pair having a quality score satisfying the quality threshold and a diversity score satisfying a diversity threshold, the second query of the query pair is associated with the first query of the query pair as a candidate refinement for the first query.

Generating query refinements from user preference data

A system, computer-readable storage medium storing at least one program, and a computer-implemented method for identifying chains of related search queries is presented. Historical search query data is obtained. Chains of related search queries issued by users and search results corresponding to last related search queries in the chains of related search queries that were selected by the users are identified from the historical search query data, where each related search query in a chain of related search queries except for a last related search query in the chain of related search queries violates a search result selection criterion. The chains of related search queries are aggregated into groups, where a respective group has a common first search query and a common search result corresponding to at least one last related search query that was selected by the users. Aggregate data for the groups are stored in a query database.

Populating query suggestion database using chains of related search queries

A computer-implemented method for identifying search results satisfying a search query received from a user includes obtaining a set of ranked search results that satisfies the search query, and identifying, using historical search query data, a search result that was selected by users who issued the search query and that corresponds to at least one related search query in at least one chain of related search queries that are related to the search query. The method further includes determining, using the historical search query data, a frequency that the search query led to a selection of the identified search result; inserting the identified search result into the set of ranked search results based on the frequency that the search query led to the selection of the identified search result; and returning the set of ranked search results to the user.

Supplementing search results with historically selected search results of related queries

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining word boundary likelihoods in potentially incomplete text. In one aspect, a method includes selecting query sequences from the query, each query sequence being at least a portion of a word n-gram, the word n-gram being a subsequence of up to n words selected from the second sequence of words of the query, and for each query sequence: determining one or more query sequence keys for the query sequence; determining at least one of a word boundary count and a non-word boundary count for each query sequence key, each word-boundary count and non-word boundary count being dependent on the context of the query sequence; and associating, in a data storage device, the at least one word boundary count and non-word boundary counts with each query sequence key.

Determining word boundary likelihoods in potentially incomplete text

A computer-implemented method for proving complete query suggestions, performed by a server system, includes receiving a partial search query from a client system. A database of complete search queries is searched to identify a first set of matching complete search queries. If the first set of matching complete search queries fails to satisfy predefined criteria (e.g., with respect to the quantity and/or quality of the matching complete search queries), a rewrite of the received partial search query is performed to produce a first set of rewritten partial search queries. The database of complete search queries is searched to identify a second set of complete search queries matching any of the first set of rewritten partial search queries. At least a subset of the second set of matching complete queries are sent to the client system.

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Sunnyvale, CA, United States of America

Abhinandan Sujit Das