  Determinantal point processes (DPPs) have received significant attention in
the recent years as an elegant model for a variety of machine learning tasks,
due to their ability to elegantly model set diversity and item quality or
popularity. Recent work has shown that DPPs can be effective models for product
recommendation and basket completion tasks. We present an enhanced DPP model
that is specialized for the task of basket completion, the multi-task DPP. We
view the basket completion problem as a multi-class classification problem, and
leverage ideas from tensor factorization and multi-class classification to
design the multi-task DPP model. We evaluate our model on several real-world
datasets, and find that the multi-task DPP provides significantly better
predictive quality than a number of state-of-the-art models.
