Real-world Recommender Systems are often facing drifts in users’ preferences and shifts in items’ perception or use. Traditional stateof- the-art methods based on matrix factorization are not originally designed to cope with these dynamic and time-varying e ects and, indeed, could perform rather poorly if there is no “reactive”, on-line model update.
In this paper, we propose a new incremental matrix completion method, that automatically allows the factors related to both users and items to adapt on-line” to such drifts. Model updates are based on a temporal regularization, ensuring smoothness and consistency over time, while leading to very ecient, easily scalable algebraic computations. Several experiments on real-world data sets show that these adaptation mechanisms signi cantly improve the quality of recommendations compared to the static setting and other standard on-line adaptive algorithms.
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