# Deep Coevolutionary Network: Embedding User and Item Features for Recommendation

arXiv: https://arxiv.org/abs/1609.03675 · HTML: https://mlsanity.com/paper/1609.03675/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2016.

## Built on by (most cited first, 30 of 33)

- [TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning](https://mlsanity.com/paper/2105.07944/) (2021)
- [A Survey on Embedding Dynamic Graphs](https://mlsanity.com/paper/2101.01229/) (2021)
- [Dynamic Graph Collaborative Filtering](https://mlsanity.com/paper/2101.02844/) (2021)
- [TIGER: Temporal Interaction Graph Embedding with Restarts](https://mlsanity.com/paper/2302.06057/) (2023)
- [AutoSeqRec: Autoencoder for Efficient Sequential Recommendation](https://mlsanity.com/paper/2308.06878/) (2023)
- [Prompt Learning on Temporal Interaction Graphs](https://mlsanity.com/paper/2402.06326/) (2024)
- [Parameter-free Dynamic Graph Embedding for Link Prediction](https://mlsanity.com/paper/2210.08189/) (2022)
- [Triple Structural Information Modelling for Accurate, Explainable and Interactive Recommendation](https://mlsanity.com/paper/2304.11528/) (2023)
- [Self-Explainable Temporal Graph Networks based on Graph Information Bottleneck](https://mlsanity.com/paper/2406.13214/) (2024)
- [Dynamic Hawkes Processes for Discovering Time-evolving Communities' States behind Diffusion Processes](https://mlsanity.com/paper/2105.11152/) (2021)
- [Deep Structural Point Process for Learning Temporal Interaction Networks](https://mlsanity.com/paper/2107.03573/) (2021)
- [Efficient Dynamic Graph Representation Learning at Scale](https://mlsanity.com/paper/2112.07768/) (2021)
- [Recurrent Temporal Revision Graph Networks](https://mlsanity.com/paper/2309.12694/) (2023)
- [Dynamic Sequential Graph Learning for Click-Through Rate Prediction](https://mlsanity.com/paper/2109.12541/) (2021)
- [Dynamic Representation Learning with Temporal Point Processes for Higher-Order Interaction Forecasting](https://mlsanity.com/paper/2112.10154/) (2021)
- [SINCERE: Sequential Interaction Networks representation learning on Co-Evolving RiEmannian manifolds](https://mlsanity.com/paper/2305.03883/) (2023)
- [iLoRE: Dynamic Graph Representation with Instant Long-term Modeling and Re-occurrence Preservation](https://mlsanity.com/paper/2309.02012/) (2023)
- [IACN: Influence-aware and Attention-based Co-evolutionary Network for Recommendation](https://mlsanity.com/paper/2103.02866/) (2021)
- [Dynamic Customer Embeddings for Financial Service Applications](https://mlsanity.com/paper/2106.11880/) (2021)
- [Contrastive Sequential Interaction Network Learning on Co-Evolving Riemannian Spaces](https://mlsanity.com/paper/2401.01243/) (2024)
- [Learning Student Interest Trajectory for MOOCThread Recommendation](https://mlsanity.com/paper/2101.05625/) (2021)
- [SimHawNet: A Modified Hawkes Process for Temporal Network Simulation](https://mlsanity.com/paper/2203.07260/) (2022)
- [From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs](https://mlsanity.com/paper/2307.08433/) (2023)
- [CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task Learning](https://mlsanity.com/paper/2309.04802/) (2023)
- [ForeSeer: Product Aspect Forecasting Using Temporal Graph Embedding](https://mlsanity.com/paper/2310.04865/) (2023)
- [Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review](https://mlsanity.com/paper/2412.03783/) (2024)
- [Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures](https://mlsanity.com/paper/2412.21046/) (2024)
- [Representation Learning for Efficient and Effective Similarity Search and Recommendation](https://mlsanity.com/paper/2109.01815/) (2021)
- [Sequential Modelling with Applications to Music Recommendation, Fact-Checking, and Speed Reading](https://mlsanity.com/paper/2109.06736/) (2021)
- [Dynamic Healthcare Embeddings for Improving Patient Care](https://mlsanity.com/paper/2303.11563/) (2023)

## Abstract (arXiv)

Recommender systems often use latent features to explain the behaviors of users and capture the properties of items. As users interact with different items over time, user and item features can influence each other, evolve and co-evolve over time. The compatibility of user and item's feature further influence the future interaction between users and items. Recently, point process based models have been proposed in the literature aiming to capture the temporally evolving nature of these latent features. However, these models often make strong parametric assumptions about the evolution process of the user and item latent features, which may not reflect the reality, and has limited power in expressing the complex and nonlinear dynamics underlying these processes. To address these limitations, we propose a novel deep coevolutionary network model (DeepCoevolve), for learning user and item features based on their interaction graph. DeepCoevolve use recurrent neural network (RNN) over evolving networks to define the intensity function in point processes, which allows the model to capture complex mutual influence between users and items, and the feature evolution over time. We also develop an efficient procedure for training the model parameters, and show that the learned models lead to significant improvements in recommendation and activity prediction compared to previous state-of-the-arts parametric models.