Abstract: A system and method are disclosed for identifying the tasks that a user is attempting to perform when submitting a series of search queries. A task may be categorized hierarchically into a mission and a goal in order to classify a series of queries and determine the user's intent. The user's mission may be an extended task and the user's goal may be a specific task. A pair of queries may be analyzed to determine whether those queries have the same mission and/or the same goal. For a series of queries, boundaries may be identified between the queries to identify where a user has changed her goal or changed her mission. The identification of goals, missions and boundaries may be automated based on an analysis of features of the queries that are compared.

Abstract: Systems and methods according to embodiments leverage click data to predict a relevance judgment for a given query-content item pair. An initial training phase utilize a training set of query-content item pairs coupled with click data and relevance data (e.g., relevance judgments or labels) to train a model of the relationship between relevance and clicks. Accordingly, given an unlabeled query-content item pair as input to the model, a relevance judgment or label is provided. Theses relevance labels, in turn, may be used in conjunction with query-content item pairs with which they are associated to train a model to determine a content item relevance function. When a user provides a query to a given search engine, the search engine applies the content item relevance function to the query and content items in a responsive result set to provide a relevance ordered result set to the user.

Abstract: Systems and methods according to embodiments leverage click data to predict a relevance judgment for a given query-content item pair. An initial training phase utilize a training set of query-content item pairs coupled with click data and relevance data (e.g., relevance judgments or labels) to train a model of the relationship between relevance and clicks. Accordingly, given an unlabeled query-content item pair as input to the model, a relevance judgment or label is provided. Theses relevance labels, in turn, may be used in conjunction with query-content item pairs with which they are associated to train a model to determine a content item relevance function. When a user provides a query to a given search engine, the search engine applies the content item relevance function to the query and content items in a responsive result set to provide a relevance ordered result set to the user.

Abstract: A system is disclosed for expanding a user query based on user learned popularity rankings. User queries often have no exact match, resulting in a user having to refine or abandon the search. The disclosed system obtains popularity rankings based on the content of a user query. The system expands the user query based on the popularity rankings. Thereafter, query results based on the expanded user query may be provided to the user. The system also regularly learns from user behavior and adapts the popularity rankings according to the learned information.

Abstract: The present invention is directed towards systems and methods for generating one or more related queries with respect to a given query. The method may comprise receiving a query comprising one or more terms, generating a corpus of candidate reformulations and identifying one or more queries or terms from the corpus of candidate reformulations related to the received query. One or more related queries are generated on the basis of the identified one or more queries or terms from the corpus of candidate reformulations.

Abstract: The present invention is directed towards systems and methods for generating one or more related queries with respect to a given query. The method may comprise receiving a query comprising one or more terms, generating a corpus of candidate reformulations and identifying one or more queries or terms from the corpus of candidate reformulations related to the received query. One or more related queries are generated on the basis of the identified one or more queries or terms from the corpus of candidate reformulations.

Abstract: A system and method are disclosed for identifying similar queries. A user query may be compared with known search keywords. The user query may be a Chinese related query, which is converted into a different form before comparing with other converted queries or keywords. A similarity score based on different features may be used for comparing the queries.

Abstract: The present invention relates to systems and methods for identifying one or more queries related to a given query. The method of the present invention comprises receiving a query written according to one or more writing systems of a language with multiple writing systems. A candidate set of queries written according to one or more writing systems of the language with multiple writing systems is identified. A score is calculated for the one or more queries in the candidate set indicating the similarity of the one or more queries with respect to the query received.

Abstract: A system and method for receiving a query and determining candidate alternate queries. A query may be parsed into concepts and then candidates for alternate terms may be determined for either the whole query or the determined concepts. Candidates are scored for their respective similarity to the original query. After scoring, at least one candidate is chosen and may be used as a possible alternate query choice for a user or used to serve bidded advertisements to the user.

Abstract: A system and method for receiving a query and determining candidate alternate queries. A query may be parsed into concepts and then candidates for alternate terms may be determined for either the whole query or the determined concepts. Candidates are scored for their respective similarity to the original query. After scoring, at least one candidate is chosen and may be used as a possible alternate query choice for a user or used to serve bidded advertisements to the user.

Abstract: In certain implementations, a system and/or method is provided for providing search results in response to a search query. In this implementation a temporal profile of the search query is built from temporal data associated with documents retrieved in response to the search query. From features of the temporal profile it may determined whether the search query would benefit from relevance feedback from a user. If there is a determination that the search query will benefit from the relevance feedback, relevance feedback is sought from the user. Search results are provided based on the relevance feedback.

Abstract: A familiarity level classifier comprises a stopwords engine for conducting a stopwords analysis of stopwords, e.g., introductory level stopwords and advanced level stopwords, in a document, e.g., a website; and a familiarity level classifier module for generating a document familiarity level based on the stopwords analysis. The classifier may be in an indexing module, a search engine, a user computer, or elsewhere in a computer network. The classifier may also include a reading level engine for conducting a reading level analysis of the document, and wherein the familiarity level classifier module is configured to generate the familiarity level also based on the reading level analysis. The classifier may also include a document features engine for conducting a feature analysis of the document, and wherein the familiarity level classifier module is configured to generate the document familiarity level also based on the feature analysis.

Abstract: A method for facilitating development of a document classification function comprises selecting a feature of a document, the feature being less than an entirety of the document; presenting the feature to a human subject; asking the human subject for a feature relevance value of the feature; and generating a classification function using the feature relevance value. The method may also include the steps of presenting the document to the human subject at the same time as presenting the feature; asking the human subject for document relevance value that measures relevance of the document to a category; and wherein the generating the classification function also uses the document relevance value.