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 based on the user's vector, bias, and interaction.