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.

该研究提出了一种耦合的循环神经网络模型 JODIE，它可以学习用户和项目的嵌入轨迹，并且在未来预测用户 - 项目的交互和状态变化，实验结果证明该模型在这些任务中优于六个现有算法至少 20％。

在时间交互网络中预测动态嵌入轨迹

Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks

Modeling a sequence of interactions between users and items (e.g., products,
posts, or courses) is crucial in domains such as e-commerce, social networking,
and education to predict future interactions. Representation learning presents
an attractive solution to model the dynamic evolution of user and item
properties, where each user/item can be embedded in a euclidean space and its
evolution can be modeled by dynamic changes in embedding. However, existing
embedding methods either generate static embeddings, treat users and items
independently, or are not scalable.
Here we present JODIE, a coupled recurrent model to jointly learn the dynamic
embeddings of users and items from a sequence of user-item interactions. JODIE
has three components. First, the update component updates the user and item
embedding from each interaction using their previous embeddings with the two
mutually-recursive Recurrent Neural Networks. Second, a novel projection
component is trained to forecast the embedding of users at any future time.
Finally, the prediction component directly predicts the embedding of the item
in a future interaction. For models that learn from a sequence of interactions,
traditional training data batching cannot be done due to complex user-user
dependencies. Therefore, we present a novel batching algorithm called t-Batch
that generates time-consistent batches of training data that can run in
parallel, giving massive speed-up.
We conduct six experiments 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
up to 22.4%. Moreover, we show that JODIE is highly scalable and up to 9.2x
faster than comparable models. As an additional experiment, we illustrate that
JODIE can predict student drop-out from courses five interactions in advance.

JODIE 是一种耦合的递归模型，用于共同学习用户和物品的动态嵌入以预测未来互动。在四个真实世界数据集上的六个实验中，JODIE 在未来交互预测和状态变化预测任务中优于六个最先进算法，且高度可扩展，比可比模型快 9.2 倍。