Generating item clusters based on aggregated search history data

Abstract

The present disclosure provides computer-implemented systems and processes for clustering items and improving the utility of item recommendations. One process involves applying a clustering algorithm to users' search session queries over periods of time to generate query clusters comprising correlated query terms. Correlations may be based on, among other things, the frequency of which query term pairs appear together in a single search session. The generated query clusters may be used to generate item descriptor clusters indicative of items and/or types of items that may be complementary. Other criteria may be applied to the query and item clusters to generate variant clusters. For example, information such as related brands, market segments, and other data may be applied to item descriptor clusters to generate item clusters that include complementary items associated with or targeted for particular demographics. Item descriptor clusters and item clusters can be used to improve the item recommendations.