Abstract: A system and method for personalized search ranking may use a user's feedback to immediately reorder search results for this particular user so as to improve click-through rate. Upon receiving a query including one or more words, a search engine may identify a list of search results and display the search results on a search result page. A machine-learning module may collect information about a user's browsing activities on the result page, update estimates of relevance of the search results, and reorder the search result list to personalize it for the user.

Abstract: A system and method for personalized search ranking may use a user's feedback to immediately reorder search results for this particular user so as to improve click-through rate. Upon receiving a query including one or more words, a search engine may identify a list of search results and display the search results on a search result page. A machine-learning module may collect information about a user's browsing activities on the result page, update estimates of relevance of the search results, and reorder the search result list to personalize it for the user.

Abstract: A system comprises a machine readable storage medium having an index that, given a set of inputs, a set of outputs, a set of input categories, and a scoring rule, provides an ordered subset of the outputs for each input category. The outputs within each subset are ordered by predicted score with respect to an input from one of the input categories. At least one processor is capable of receiving an input corresponding to at least one of the set of input categories. The processor is configured for scoring a reduced set of outputs against the received input using the scoring rule. The reduced set of outputs includes a union of the subsets of outputs associated with each input category to which the received inputs correspond. The processor is configured for outputting a list including a subset of the reduced set of outputs having the highest scores.