Abstract
AbstractEnsemble methods have been successfully used in the past to improve recommender systems; however, they have never been studied with incremental recommendation algorithms. Many online recommender systems deal with continuous, potentially fast, and unbounded flows of data—big data streams—and often need to be responsive to fresh user feedback, adjusting recommendations accordingly. This is clear in tasks such as social network feeds, news recommender systems, automatic playlist completion, and other similar applications. Batch ensemble approaches are not suitable to perform continuous learning, given the complexity of retraining new models on demand. In this paper, we adapt a general purpose online bagging algorithm for top‐N recommendation tasks and propose two novel online bagging methods specifically tailored for recommender systems. We evaluate the three approaches, using an incremental matrix factorization algorithm for top‐N recommendation with positive‐only user feedback data as the base model. Our results show that online bagging is able to improve accuracy up to 55% over the baseline, with manageable computational overhead.
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