Collaborative filtering algorithms are used to predict whether a given individual might like, or purchase, an item. One popular approach is to find a set of individuals (e.g. customers) whose item preferences (ratings) are similar to those of the given individual over a number of different items. The attention then shifts to an item which the given individual has NOT purchased or rated. The ratings of the similar group are aggregated for that missing item, and that aggregate rating is then used as the prediction for the given individual.