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.