Google Inc.

Pandurang P. Nayak

Alexis J. Battle

David R. Bailey

Benedict a. Gomes

Alexis J Battle

David Robert Bailey

Corin Anderson

P Pandurang Nayak

Benedict A Gomes

Jeffrey Adgate Dean

David Ariel Cohn

Paul Fontes

John Ogden Lamping

Kushal Dave

Casey Whitelaw

Barbara Engelhardt

Carrie Elizabeth Grimes

Pandurang P Nayak

Barbara Englehardt

David R Bailey

Systems and methods for deriving and using an interaction profile are described. In one described method, a plurality of metrics indicating a level of satisfaction for search results is determined. The metrics comprise at least one of click-duration data, multiple-click data, and query-refinement data. The values of the metrics for a plurality of instances of an object, such as search results from a search engine, are determined. An interaction profile for the object, based at least in part on the values of the metrics for a plurality of instances of the first object, is then determined. This interaction profile may be used in a variety of ways, such as determining the quality of ranking algorithms and detecting undesirable search results.

Deriving and using interaction profiles

A system generates a model based on feature data relating to different features of a link from a linking document to a linked document and user behavior data relating to navigational actions associated with the link. The system also assigns a rank to a document based on the model.

Ranking documents based on user behavior and/or feature data

An information retrieval system includes a query revision architecture that integrates multiple different query revisers, each implementing one or more query revision strategies. A revision server receives a user's query, and interfaces with the various query revisers, each of which generates one or more potential revised queries. The revision server evaluates the potential revised queries, and selects one or more of them to provide to the user. A session-based reviser suggests one or more revised queries, given a first query, by calculating an expected utility for the revised query. The expected utility is calculated as the product of a frequency of occurrence of the query pair and an increase in quality of the revised query over the first query.

Estimating confidence for query revision models

A system provides a list of search results, where one of the search results in the list of search results includes a snippet from a corresponding search result document. The system receives selection of the snippet and provides an expanded snippet based on the selection of the snippet. The expanded snippet includes the snippet and other text from a subset of the search result document.

Expanded text excerpts

An information retrieval system includes a query revision architecture providing one or more query revisers, each of which implements a query revision strategy. A query rank reviser suggests known highly-ranked queries as revisions to a first query by initially assigning a rank to all queries, and identifying a set of known highly-ranked queries (KHRQ). Queries with a strong probability of being revised to a KHRQ are identified as nearby queries (NQ). Alternative queries that are KHRQs are provided as candidate revisions for a given query. For alternative queries that are NQs, the corresponding known highly-ranked queries are provided as candidate revisions.

Query revision using known highly-ranked queries

Systems and methods for providing refinements to queries based upon the context of a refinement indication. Refinements, for example, can be related to a previous search query and can be provided to the user based upon receipt of a refinement indication.

Contextual display of query refinements

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Palo Alto, CA, United States of America

Alexis J Battle