  Many machine learning systems utilize latent factors as internal
representations for making predictions. Since these latent factors are largely
uninterpreted, however, 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. We illustrate the use of LFI interpretations on the MovieLens dataset,
integrating auxiliary features from IMDB and DB tropes, and show that latent
factors can be predicted with sufficient accuracy for replicating the
predictions of the true model.
