Client Time Series Model: a Multi-Target Recommender System based on Temporally-Masked Encoders

The foundation of our recommendation stack is a scoring model we call p(sale), which estimates the probability that any given client will purchase any given item. This model has gone through many iterations over the years, from a mixed effects model, to a matrix factorization model, and now to a novel sequence-based model. Internally we call this the Client Time Series Model (aka CTSM) because of its focus on the time-domain of client interactions. This post details our new model, which is a significant improvement for both the quality of our recommendations and the maintainability of our systems. Before setting out to develop our new model, it was clear that the evolution of our business necessitated a change to our modeling approach.