Matrix Factorization for Collaborative Filtering Is Just Solving an Adjoint Latent Dirichlet Allocation Model After All

Matrix factorization-based methods are among the most popular methods for collaborative filtering tasks with implicit feedback. The most effective of these methods do not apply sign constraints, such as non-negativity, to their factors. Despite their simplicity, the latent factors for users and items lack interpretability, which is becoming an increasingly important requirement. In this work, we provide a theoretical link between unconstrained and the interpretable non-negative matrix factorization in terms of the personalized ranking induced by these methods. We also introduce a novel, latent Dirichlet allocation-inspired model for recommenders and extend our theoretical link to also allow the interpretation of an unconstrained matrix factorization as an adjoint formulation of our new model. Our experiments indicate that this novel approach represents the unknown processes of implicit user-item interactions in the real world much better than unconstrained matrix factorization while being interpretable.

This paper was presented at the 15th ACM Conference on Recommender Systems (RecSys 2021) in Amsterdam. A pre-recorded video of my presentation is generously provided by ACM and also the slides are available. The recording of the presentation at RecSys22 is available here: