Abstract: A computer system for detecting associations between items may include a probabilistic analysis component that accesses user data having information about item selections by a plurality of users. The probabilistic analysis component may programmatically generate associations between certain items by determining a first number of users who selected the items and by estimating a probability that a second number of users would have selected the items due to random chance. The probabilistic analysis component may estimate this probability based at least partly on a number of item selections made by one or more of the users.

Abstract: A search engine system assists users in refining their searches by suggesting previously-submitted search phrases to such users. The search phrases are selected to suggest based on collected data regarding search behaviors of search engine users. In one embodiment, candidate search phrases are scored and selected to suggest based at least in-part on (a) a frequency with which each search phrase has been submitted, and/or (b) an evaluation of the “usefulness” of each search phrase, as reflected by post-query-submission actions of users who submitted the search phrase.

Abstract: A computer-implemented process identifies useful alternative spellings of search strings submitted to a search engine. The process takes into consideration spelling changes made by users, as detected by programmatically analyzing search string submissions of a population of search engine users. In one embodiment, an assessment of whether a second search string represents a useful alternative spelling of a first search string takes into consideration (1) an edit distance between the first and second search strings, and (2) a likelihood that a user who submits the first search string will thereafter submit the second search string, as determined by monitoring and analyzing actions of users.