  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 learn from data streams. We evaluate online bagging
with an incremental matrix factorization algorithm for top-N recommendation
with positive-only -- binary -- ratings. Our results show that online bagging
is able to improve accuracy up to 35% over the baseline, with small
computational overhead.
