# Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks

## TL;DR

JODIE is a novel recurrent neural network model that learns and predicts the future trajectories of user and item embeddings in dynamic interaction networks, significantly improving prediction accuracy and scalability.

## Contribution

It introduces a coupled RNN architecture with a projection operator for future embedding estimation and a scalable t-Batch training algorithm.

## Key findings

- JODIE outperforms six state-of-the-art algorithms in interaction prediction.
- JODIE achieves at least 20% better accuracy in future interaction prediction.
- The t-Batch algorithm speeds up training by 9 times.

## Abstract

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.