Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 confidence estimator and method provide the ability to improve the likelihood of success of suggested revised queries derived from various revision strategies. This is accomplished by tracking user queries, query revision links, results associated with revised queries, and various features of the original query and revised queries. This data is then analyzed using a predictive model to generate a set of rules that can be used to estimate the likelihood of a revised query being a successful revision for a given query.

Abstract: 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.