1. A computer implemented method for large-scale behavioral targeting, the method comprising: receiving, using a computer processor, training data that is processed raw data of user behavior; generating, using a computer processor, selected features by performing feature selection on the training data; generating, using a computer processor, feature vectors from the selected features; initializing, using a computer processor, weights of a behavioral targeting model, wherein the behavioral targeting model is based on a Poisson regression, by: scanning the feature vectors once, allocating each of the weights as a normalized co-occurrence of a target and a feature, generating a weight matrix whose dimensionality is a total number of targets by a total number of features, generating a plurality of weight vectors for each of the selected features from the weight matrix; and updating, using a computer processor, the weight vectors of the behavioral targeting model by scanning iteratively the feature vectors using a multiplicative recurrence.