Improving Incremental Recommenders with Online Bagging

Online recommender systems often deal with continuous, potentially fast and unbounded flows of data. Ensemble methods for recommender systems have been used in the past in batch algorithms, however they have never been studied with incremental algorithms, that are capable of processing those data streams on the fly. We propose online bagging, using an incremental matrix factorization algorithm for positiveonly data streams. Using prequential evaluation, we show that bagging is able to improve accuracy more than 35% over the baseline with small computational overhead.