Latent Factor Interpretations for Collaborative Filtering
This addresses the need for interpretability in recommendation systems, which is incremental as it builds on existing matrix factorization methods by adding interpretability layers.
The paper tackles the problem of uninterpretable latent factors in collaborative filtering for recommendations by introducing Latent Factor Interpretation (LFI), a method that interprets these factors using human-understandable features, and demonstrates its application on the MovieLens dataset with auxiliary data to replicate model predictions.
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.