Abstract: The present invention is directed towards systems and methods for providing search assistance technologies based on a user's search self-efficacy and search frustration. The method according to one embodiment of the present invention comprises receiving a search query from a user. The method then calculates the user's search self-efficacy and calculates the user's frustration with the current information task. The method then identifies a plurality of search assistance technologies based on the user's search self-efficacy and frustration. Finally, the method provides a search engine results page comprising a plurality of search results and the identified plurality of search assistance technologies.

Abstract: The present invention is directed towards systems and methods for providing search assistance technologies based on a user's search self-efficacy and search frustration. The method according to one embodiment of the present invention comprises receiving a search query from a user. The method then calculates the user's search self-efficacy and calculates the user's frustration with the current information task. The method then identifies a plurality of search assistance technologies based on the user's search self-efficacy and frustration. Finally, the method provides a search engine results page comprising a plurality of search results and the identified plurality of search assistance technologies.

Abstract: The likely relevance of each term of a search-engine query of two or more terms is determined by their deletion probability scores. If the deletion probability scores are significantly different, the deletion probability score can be used to return targeted ads related to the more relevant term or terms along with the search results. Deletion probability scores are determined by first gathering historical records of search queries of two or more terms in which a subsequent query was submitted by the same user after one or more of the terms had been deleted. The deletion probability score for a particular term of a search query is calculated as the ratio of the number of times that particular term was itself deleted prior to a subsequent search by the same user divided by the number of times there were subsequent search queries by the same user in which any term or terms including that given term was deleted by the same user prior to the subsequent search. Terms are not limited to individual alphabetic words.

Abstract: The likely relevance of each term of a search-engine query of two or more terms is determined by their deletion probability scores. If the deletion probability scores are significantly different, the deletion probability score can be used to return targeted ads related to the more relevant term or terms along with the search results. Deletion probability scores are determined by first gathering historical records of search queries of two or more terms in which a subsequent query was submitted by the same user after one or more of the terms had been deleted. The deletion probability score for a particular term of a search query is calculated as the ratio of the number of times that particular term was itself deleted prior to a subsequent search by the same user divided by the number of times there were subsequent search queries by the same user in which any term or terms including that given term was deleted by the same user prior to the subsequent search. Terms are not limited to individual alphabetic words.