  Modeling sequential interactions between users and items/products is crucial
in domains such as e-commerce, social networking, and education. Representation
learning presents an attractive opportunity to model the dynamic evolution of
users and items, where each user/item can be embedded in a Euclidean space and
its evolution can be modeled by an embedding trajectory in this space. However,
existing dynamic embedding methods generate embeddings only when users take
actions and do not explicitly model the future trajectory of the user/item in
the embedding space. Here we propose JODIE, a coupled recurrent neural network
model that learns the embedding trajectories of users and items. JODIE employs
two recurrent neural networks to update the embedding of a user and an item at
every interaction. Crucially, JODIE also models the future embedding trajectory
of a user/item. To this end, it introduces a novel projection operator that
learns to estimate the embedding of the user at any time in the future. These
estimated embeddings are then used to predict future user-item interactions. To
make the method scalable, we develop a t-Batch algorithm that creates
time-consistent batches and leads to 9x faster training. We conduct six
experiments to validate JODIE on two prediction tasks---future interaction
prediction and state change prediction---using four real-world datasets. We
show that JODIE outperforms six state-of-the-art algorithms in these tasks by
at least 20% in predicting future interactions and 12% in state change
prediction.
