A Missing Information Loss function for implicit feedback datasets
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Latent factor models with implicit feedback typically treat unobserved user-item interactions (i.e. missing information) as negative feedback. This is frequently done either through negative sampling (point-wise loss) or with a ranking loss function (pair- or list-wise estimation). Since a zero preference recommendation is a valid solution for most common objective functions, regarding unknown values as actual zeros results in users having a zero preference recommendation for most of the available items. In this paper we propose a novel objective function, the Missing Information Loss (MIL) function, that explicitly forbids treating unobserved user-item interactions as positive or negative feedback. We apply this loss to a user--based Denoising Autoencoder and compare it with other known objective functions such as cross-entropy (both point-- and pair--wise) or the recently proposed multinomial log-likelihood. The MIL function achieves best results in ranking-aware metrics when applied to the Movielens-20M and Netflix datasets, slightly above those obtained with cross-entropy in point-wise estimation. Furthermore, such a competitive performance is obtained while recommending popular items less frequently, a valuable feature for Recommender Systems with a large catalogue of products.
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