Recommendation generation for infrequently accessed items

Abstract

Systems and methods are disclosed for generating recommendation rules based on the attributes of items that are purchased together at a threshold rate. The attributes of the items may be extracted from item-detail content associated with the items. Using a count of the frequency with which pairs of items include pairs of attributes, a recommendation rule can be created that recommends items with particular attributes to users who access other items with particular attributes. Further, using the recommendation rules, items may be selected for recommendation to users who access an item that lacks historical access data from which to generate recommendations solving the 'cold-start' problem. Moreover, negative rules may be generated based on historical access data and attributes of items purchased and/or not purchased together at a threshold rate to prevent the recommendation of particular items to users who access items associated with the negative rules.