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Recommender systems research tends to evaluate model performance offline andon randomly sampled targets, yet the same systems are later used to predictuser behavior sequentially from a fixed point in time. Simulating onlinerecommender system performance is notoriously difficult and the discrepancybetween online and offline behaviors is typically not accounted for in offlineevaluations. This disparity permits weaknesses to go unnoticed until the modelis deployed in a production setting. In this paper, we first demonstrate howomitting temporal context when evaluating recommender system performance leadsto false confidence. To overcome this, we postulate that offline evaluationprotocols can only model real-life use-cases if they account for temporalcontext. Next, we propose a training procedure to further embed the temporalcontext in existing models. We use a multi-objective approach to introducetemporal context into traditionally time-unaware recommender systems andconfirm its advantage via the proposed evaluation protocol. Finally, wevalidate that the Pareto Fronts obtained with the added objective dominatethose produced by state-of-the-art models that are only optimized for accuracyon three real-world publicly available datasets. The results show thatincluding our temporal objective can improve recall@20 by up to 20%.

Modeling Online Behavior in Recommender Systems: The Importance of Temporal Context