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Abstract
<p class="first" id="P1">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
<i>JODIE</i>, a coupled recurrent neural network model that learns the embedding trajectories
of users and items.
<i>JODIE</i> employs two recurrent neural networks to update the embedding of a user
and an item
at every interaction. Crucially,
<i>JODIE</i> 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
<i>t-Batch</i> algorithm that creates time-consistent batches and leads to 9× faster
training. We
conduct six experiments to validate
<i>JODIE</i> on two prediction tasks— future interaction prediction and state change
prediction—using
four real-world datasets. We show that
<i>JODIE</i> outperforms six state-of-the-art algorithms in these tasks by at least
20% in predicting
future interactions and 12% in state change prediction.
</p>