A method and a system for re-ranking an existing result set of documents. A user starts a search by entering search term(s). The search term(s) is (are) transferred to a search engine which generates a result set ranked by the search term(s). The search engine, in parallel, automatically retrieves context information from returned result set which is related to the original set of documents. The search engine presents the context information to the user and asks for a feedback. The user performs a weighting of the presented context information in a range from "important" to "non-important". The result set is then re-ranked with the user-weighted context information to increase the "rank distance" of important and non important documents. The documents that are on top of the list (highest context-weighted ranking value) represent the desired information.

A method prioritizes search results provided to a client according to client satisfaction with previous search results. The method tracks client activity with respect to the previous search results, determines individual client satisfaction for each item according to the tracked client activity, and provides one or more of the items determined to have a high client satisfaction. A user interface provides prioritized search results to a client according to client satisfaction with a previous search similar to the current search. The user interface comprises an item display of at least one item determined to have a high client satisfaction level and at least one item not determined to have a high client satisfaction level.

A system ranks documents based, at least in part, on a ranking model. The ranking model may be generated to predict the likelihood that a document will be selected. The system may receive a search query and identify documents relating to the search query. The system may then rank the documents based, at least in part, on the ranking model and form search results for the search query from the ranked documents.

Improving ranking algorithms for information retrieval. The ranking algorithms operate on search results obtained from a search engine. Input information including information describing a first ranking algorithm, a first score associated with the first ranking algorithm, a second ranking algorithm, a second score associated with the second ranking algorithm, and causal information relating a difference between the first ranking algorithm and the second ranking algorithm with a difference between the first score and the second score is received. An optimizing algorithm is applied to the received input information to identify an optimal ranking algorithm having an optimal score. The optimal ranking algorithm is defined by a plurality of parameters and a plurality of weights associated with the plurality of parameters.

Rating information retrieval algorithms. A query is received and submitted to a search engine for execution on an index file. A list of index documents and a plurality of attributes are received from the search engine in response to the submitted query. A portion of the received list of index documents and the received plurality of attributes are stored in a subindex file. The received query is executed on the subindex file to obtain a list of subindex documents. The obtained list of subindex documents is ranked by a particular ranking algorithm using the attributes associated therewith. The list of ranked subindex documents is compared with a list of subjectively ranked documents to generate a score for the particular ranking algorithm. The generated score represents a degree of correlation between the list of ranked subindex documents and the list of subjectively ranked documents.