Latent Factor Interpretations for Collaborative Filtering
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Many machine learning systems utilize latent factors as internal representations for making predictions. However, since these latent factors are largely uninterpreted, predictions made using them are opaque. Collaborative filtering via matrix factorization is a prime example of such an algorithm that uses uninterpreted latent features, and yet has seen widespread adoption for many recommendation tasks. We present Latent Factor Interpretation (LFI), a method for interpreting models by leveraging interpretations of latent factors in terms of human-understandable features. The interpretation of latent factors can then replace the uninterpreted latent factors, resulting in a new model that expresses predictions in terms of interpretable features. This new model can then be interpreted using recently developed model explanation techniques. In this paper, we develop LFI for collaborative filtering based recommender systems, which are particularly challenging from an interpretation perspective. We illustrate the use of LFI interpretations on the MovieLens dataset demonstrating that latent factors can be predicted with enough accuracy for accurately replicating the predictions of the true model. Further, we demonstrate the accuracy of interpretations by applying the methodology to a collaborative recommender system using DB tropes and IMDB data and synthetic user preferences.
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