Abstract: Aspects of the subject matter described herein relate to predicting and using search engine switching behavior. In aspects, switching components receive a representation of user interactions with at least one browser. The switching components derive information from the representation that is useful in predicting whether a user will switch search engines. The derived information and information about a user's current interaction with a browser is then used by a switch predictor to predict whether the user will switch search engines. This prediction may be used in a variety of ways examples of which are given herein.

Abstract: Aspects of the subject matter described herein relate to predicting and using search engine switching behavior. In aspects, switching components receive a representation of user interactions with at least one browser. The switching components derive information from the representation that is useful in predicting whether a user will switch search engines. The derived information and information about a user's current interaction with a browser is then used by a switch predictor to predict whether the user will switch search engines. This prediction may be used in a variety of ways examples of which are given herein.

Abstract: A search expertise level system and method for determining a search expertise level of a search engine user and then using that information to improve the searcher's experience. The search expertise level system and method identifies the search expertise level of the searcher based on query behavior, post-query browsing behavior, and other behaviors of the searcher. One simple and important behavior that indicates a skilled searcher is the use of advanced query syntax and operators in the query. Once the search expertise level of a searcher is known, the search engine user interface can be modified and tailored to the needs of both skilled and novice searchers. The search expertise level also can be used to rank search results, such that search results for a novice searcher are ranked differently than those for a skilled searcher. The search expertise level also can be used in advertising and marketing.

Abstract: Aspects of the subject matter described herein relate to predicting and using search engine switching behavior. In aspects, switching components receive a representation of user interactions with at least one browser. The switching components derive information from the representation that is useful in predicting whether a user will switch search engines. The derived information and information about a user's current interaction with a browser is then used by a switch predictor to predict whether the user will switch search engines. This prediction may be used in a variety of ways examples of which are given herein.

Abstract: Described is the presenting of suggested queries for web pages that are not search engine results pages, based upon the URL and/or content of a currently displayed page. The suggested query set may be dynamically extracted (locally or remotely) based upon the content of the web page, and/or obtained from a data store of per-URL suggested query sets, e.g., generated from historical logs. Also described are various techniques for generating suggested queries, and user interface mechanisms that display and allow interaction with suggested queries.

Abstract: Aspects of the subject matter described herein relate to predicting a best search engine to use for a given query. In aspects, a predictor may use various approaches to determine a best search engine for a given query. For example, the predictor may use features derived from the query itself, how well the query matches a result set returned by a search engine in response to the query, and/or information that compares the result sets returned by multiple search engines that are provided the query. In addition, other data such as user preferences, user interaction data, metadata attributes, and/or other data may be used in predicting a best search engine for a given query. In conjunction with making a prediction, the predictor may use a classifier that has been trained at a training facility.

Abstract: A system that facilitates targeted advertising is described in detail herein. The system includes a receiver component that receives user data that includes historical searching and browsing activity of a user. A profile generator component generates a user profile based at least in part upon a subset of the user data, wherein the user profile includes a plurality of keywords, wherein at least one keyword in the plurality of keywords is assigned a score that is indicative of a probability that an advertisement corresponding to the keyword will be monetized.

Abstract: A search query transformation system and method for transforming and refining a search query are described. Embodiments of the system and method use various graphical components and controls. Direct manipulation ensures that the searcher is driving the changes in the search queries using a pointing device. Embodiments of the search query transformation system and method include a search query re-weighting user interface (UI) component for graphically adjusting and re-weighting weights of search terms, and a search query term replacement UI component for graphically replacing a search term in a query or add a synonym to the query. Embodiments of the system and method also include a search query suggestion component, which provides query revision recommendations to a searcher that are tailored to the direct manipulation query refinement interface.

Abstract: Aspects of the subject matter described herein relate to predicting and using search engine switching behavior. In aspects, switching components receive a representation of user interactions with at least one browser. The switching components derive information from the representation that is useful in predicting whether a user will switch search engines. The derived information and information about a user's current interaction with a browser is then used by a switch predictor to predict whether the user will switch search engines. This prediction may be used in a variety of ways examples of which are given herein.

Abstract: A technique for the creation of synthesized results from multi-query searches to provide more relevant information to the user in a more useful format and to discard or reduce in relevancy information that is not so useful. It can determine which queries belong to the search based on parameters in the queries or results. It also provides mechanisms for supporting exploratory searches including: saving/restoring search context; search-specific query history; a “keepers” bin for storing useful results; elimination of redundant results; re-ranking of common search results; integration of searching with navigation; pivoting on search results; collaboration among multiple searchers; user-generated content; generation of hypotheses; re-executing queries and executing standing queries; multi-monitor searching and automatic preparation of search summaries.

Abstract: A search expertise level system and method for determining a search expertise level of a search engine user and then using that information to improve the searcher's experience. The search expertise level system and method identifies the search expertise level of the searcher based on query behavior, post-query browsing behavior, and other behaviors of the searcher. One simple and important behavior that indicates a skilled searcher is the use of advanced query syntax and operators in the query. Once the search expertise level of a searcher is known, the search engine user interface can be modified and tailored to the needs of both skilled and novice searchers. The search expertise level also can be used to rank search results, such that search results for a novice searcher are ranked differently than those for a skilled searcher. The search expertise level also can be used in advertising and marketing.

Abstract: A technique for the creation of synthesized results from multi-query searches to provide more relevant information to the user in a more useful format and to discard or reduce in relevancy information that is not so useful. It allows a user to define the boundaries of the exploratory search before it starts or retroactively define which queries belong to the search. It can imply which queries belong to the search based on parameters in the queries or results. It also provides mechanisms for supporting exploratory searches including: saving/restoring search context; search-specific query history; a “keepers” bin for storing useful results; elimination of redundant results; re-ranking of common search results; integration of searching with navigation; pivoting on search results; collaboration among multiple searchers; user-generated content; generation of hypotheses; re-executing queries and executing standing queries; multi-monitor searching and automatic preparation of search summaries.

Abstract: The concept of variability pertains to whether users exhibit consistent search interaction patterns, for example, in terms of interaction flow or information targeted. Methods are provided for analyzing variability, and then adapting search-related functionality (e.g., processes and/or interfaces) to account for variability characteristics, for example, to account for predictable search interaction behavior.