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This work focuses on top-k recommendation in domains where underlying datadistribution shifts overtime. We propose to learn a time-dependent bias foreach item over whatever existing recommendation engine. Such a bias learningprocess alleviates data sparsity in constructing the engine, and at the sametime captures recent trend shift observed in data. We present an alternatingoptimization framework to resolve the bias learning problem, and developmethods to handle a variety of commonly used recommendation evaluationcriteria, as well as large number of items and users in practice. The proposedalgorithm is examined, both offline and online, using real world data setscollected from the largest retailer worldwide. Empirical results demonstratethat the bias learning can almost always boost recommendation performance. Weencourage other practitioners to adopt it as a standard component inrecommender systems where temporal dynamics is a norm.

Thresholding for Top-k Recommendation with Temporal Dynamics