Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks (1908.01207v1)

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.

• The paper introduces JODIE, a model that forecasts dynamic embedding trajectories in temporal interaction networks using a coupled RNN framework.
• It employs an update-projection mechanism and a t-Batch algorithm that accelerates training by 9x while enhancing prediction accuracy by 20% for interactions and 12% for state changes.
• The work significantly advances temporal representation learning, paving the way for more precise recommendation systems and user state forecasting in dynamic environments.

Overview of "Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks"

The paper introduces JODIE, a novel approach to predicting dynamic embedding trajectories in temporal interaction networks. This model addresses the need for effective representation learning in contexts such as e-commerce and social networking, where user-item interactions evolve over time. Unlike existing methods that generate embeddings only at interaction points, JODIE aims to forecast future trajectories, thereby enabling improved prediction of future interactions.

Key Innovations and Methodology

The core innovation of JODIE lies in its use of a coupled recurrent neural network (RNN) framework, which incorporates both an update and a projection operation. The update operation employs two RNNs that recursively update the embeddings of users and items by considering the current state of their counterparts. This mutual recursion between RNNs captures the interplay between users and items effectively.

A major highlight is the projection operation, which forecasts future embedding trajectories. A novel projection operator predicts user embeddings at any future point in time, reflecting how user preferences might evolve. This projected embedding is then used to predict the user's subsequent interactions.

To scale the model efficiently, the paper introduces a batching algorithm, termed t-Batch, which constructs time-consistent batches that allow for parallel processing, significantly accelerating training by a factor of nine.

Experimental Validation

Extensive experiments validate JODIE's outperformance over six state-of-the-art algorithms across multiple datasets and tasks, including future interaction prediction and user state change forecasting. JODIE achieves at least 20% improvement in predicting interactions and 12% in predicting state changes. These results underscore the model's precision in capturing the dynamic evolution of user-item relationships.

Theoretical and Practical Implications

Theoretically, JODIE presents a framework that advances the understanding of temporal dynamics in interaction networks. Practically, its ability to project future embeddings offers robust applications in recommendation systems, social media, and educational platforms. By predicting user engagement patterns with higher accuracy and scalability, JODIE can drive enhanced decision-making for early interventions or recommendations.

Future Developments

The potential for JODIE's integration extends to various stochastic processes, where predicting entity states over time is crucial. Future work might explore the generalization of this model to other interaction types or the inclusion of additional contextual data to refine trajectory prediction. Further enhancements could involve reducing computation overhead while maintaining accuracy, possibly through innovative network design or dimensionality reduction techniques.

Conclusion

JODIE marks a significant contribution to the field of dynamic embedding learning, providing a robust mechanism for predicting interaction evolutions in temporal networks. Through its innovative architecture and validated performance gains, JODIE sets a promising direction for future research and development in temporal representation learning.