Abstract: A data mining component collectively analyzes item acquisition histories of users of an electronic catalog of items and identifies pairs of catalog items that tend to be acquired sequentially. The data mining component may also generate data regarding such sequential item acquisition patterns. For example, the data mining component may determine whether user acquisitions of the two items tend to be spaced apart in time by a characterizing time interval, and/or may determine percentages of users who have followed particular sequential acquisition patterns. Information regarding the detected sequential item acquisition patterns may be exposed to users on electronic catalog pages, and/or may be used to select the timing with which particular items are recommended to users.

Abstract: A data mining component collectively analyzes item acquisition histories of users of an electronic catalog of items to identify pairs of items that tend to be acquired sequentially. For each such item pair, the data mining component may also determine whether user acquisitions of the two items tend to be spaced apart in time by a characterizing time interval. In addition, the data mining component may calculate one or more conditional probability values reflective of the frequency with which users who acquire the first item in the pair acquire the second item after waiting for a particular interval of time. The item relationship data extracted by the data mining component may be displayed in the electronic catalog in association with corresponding catalog items to assist users in selecting items to acquire, and/or may be used to generate personalized item recommendations.