  Product recommendation systems are important for major movie studios during
the movie greenlight process and as part of machine learning personalization
pipelines. Collaborative Filtering (CF) models have proved to be effective at
powering recommender systems for online streaming services with explicit
customer feedback data. CF models do not perform well in scenarios in which
feedback data is not available, in cold start situations like new product
launches, and situations with markedly different customer tiers (e.g., high
frequency customers vs. casual customers). Generative natural language models
that create useful theme-based representations of an underlying corpus of
documents can be used to represent new product descriptions, like new movie
plots. When combined with CF, they have shown to increase the performance in
cold start situations. Outside of those cases though in which explicit customer
feedback is available, recommender engines must rely on binary purchase data,
which materially degrades performance. Fortunately, purchase data can be
combined with product descriptions to generate meaningful representations of
products and customer trajectories in a convenient product space in which
proximity represents similarity. Learning to measure the distance between
points in this space can be accomplished with a deep neural network that trains
on customer histories and on dense vectorizations of product descriptions. We
developed a system based on Collaborative (Deep) Metric Learning (CML) to
predict the purchase probabilities of new theatrical releases. We trained and
evaluated the model using a large dataset of customer histories, and tested the
model for a set of movies that were released outside of the training window.
Initial experiments show gains relative to models that do not train on
collaborative preferences.
