Embarrassingly shallow auto-encoders for dynamic collaborative filtering

Olivier Jeunen

doi:10.1007/s11257-021-09314-7

2022-01-13

PRE

Olivier Jeunen *

Jan Van Balen

Bart Goethals

DOI：10.1007/s11257-021-09314-7

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摘要

En 中文

Recent work has shown that despite their simplicity, item-based models optimised through ridge regression can attain highly competitive results on collaborative filtering tasks. As these models are analytically computable and thus forgo the need for often expensive iterative optimisation procedures, they have become an attractive choice for practitioners. Computing the closed-form ridge regression solution consists of inverting the Gramian item-item matrix, which is known to be a costly operation that scales poorly with the size of the item catalogue. Because of this bottleneck, the adoption of these methods is restricted to a specific set of problems where the number of items is modest. This can become especially problematic in real-world dynamical environments, where the model needs to keep up with incoming data to combat issues of cold start and concept drift. In this work, we propose Dynamic EASE(R): an algorithm based on the Woodbury matrix identity that incrementally updates an existing regression model when new data arrives, either approximately or exact. By exploiting a widely accepted low-rank assumption for the user-item interaction data, this allows us to target those parts of the resulting model that need updating, and avoid a costly inversion of the entire item-item matrix with every update. We theoretically and empirically show that our newly proposed methods can entail significant efficiency gains in the right settings, broadening the scope of problems for which closed-form models are an appropriate choice.

Keyword:

Recommender Systems

Collaborative Filtering

Online Learning

Ridge Regression

Linear Models