1. A method for building a latent item vector and item bias for a new item in a collaborative filtering recommendation system, wherein each user has a vector representing a user's characteristics and a bias representing the likelihood that the user will be interested in an arbitrary item, the method comprising: determining a first quantity of users to use as a training group and a second quantity of users to use as a recommendation group; receiving a first plurality of users with each user received sequentially; scoring each of the first plurality of users based on each user's vector and bias, and an best estimate latent vector to find a maximum individual score for the first plurality of users; receiving a second plurality of users with each user received sequentially; scoring each of the second plurality of users as they arrive with each user's vector and bias, and the best estimate latent vector; in response to an event selected from the group consisting of receiving a user of the second plurality of users with a score greater than the maximum individual score and receiving a final user from among the second plurality of users, selecting the user for presenting the item; and recording the selected user's interaction with the item and updating the best estimate latent vector, wherein the updating is based on the selected user's vector, bias, and the selected user's interaction with the item.