Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks

By Srijan Kumar, Xikun Zhang, Jure Leskovec. 2019

1,029 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Information Systems

In Statistical and Nonlinear Physics

Citations in the corpus

Recurrent Event Network for Reasoning over Temporal Knowledge Graphs (2019, methodology): “is (Zhou et al., 2018; Trivedi et al., 2019), knowledge graph reasoning (Trivedi et al., 2017; Nguyen et al., 2018; Kazemi et al., 2019), event forecasting (Du et al., 2016), and recommender systems (Kumar et al., 2019; You et al., 2019). Previous methods over dynamic graphs mainly focus on learning”
Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs (2019, methodology): “is (Zhou et al., 2018; Trivedi et al., 2019), knowledge graph reasoning (Trivedi et al., 2017; Nguyen et al., 2018; Kazemi et al., 2019), event forecasting (Du et al., 2016), and recommender systems (Kumar et al., 2019; You et al., 2019). Previous methods over dynamic graphs mainly focus on learning”
Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey (2020, methodology): “JODIE uses an RNN architecture to maintain the embed-dings of each node.”
Temporal Graph Networks for Deep Learning on Dynamic Graphs (2020, methodology): “Reddit and Wikipedia are bipartite interaction graphs.”
Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks (2021, methodology): “Know-E (Trivedi et al., 2017), DyRep (Trivedi et al., 2019) and JODIE (Kumar et al., 2019) use RNNs to propagate messages across interactions to update node representations.”