# Dynamic Graph Collaborative Filtering

arXiv: https://arxiv.org/abs/2101.02844 · HTML: https://mlsanity.com/paper/2101.02844/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 50 references that are in the map.
- First posted on arXiv 8 Jan 2021.

## Builds on (most cited first, 30 of 30)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Deep Graph Contrastive Representation Learning](https://mlsanity.com/paper/2006.04131/) (2020)
- [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://mlsanity.com/paper/1809.07426/) (2018)
- [Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks](https://mlsanity.com/paper/1908.01207/) (2019)
- [Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1811.00855/) (2018)
- [Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters](https://mlsanity.com/paper/2008.08692/) (2020)
- [Neural Attentive Session-based Recommendation](https://mlsanity.com/paper/1711.04725/) (2017)
- [DynGEM: Deep Embedding Method for Dynamic Graphs](https://mlsanity.com/paper/1805.11273/) (2018)
- [Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks](https://mlsanity.com/paper/2004.13826/) (2020)
- [Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction](https://mlsanity.com/paper/1910.05552/) (2019)
- [Attributed Network Embedding for Learning in a Dynamic Environment](https://mlsanity.com/paper/1706.01860/) (2017)
- [Spectral Collaborative Filtering](https://mlsanity.com/paper/1808.10523/) (2018)
- [Deep Coevolutionary Network: Embedding User and Item Features for Recommendation](https://mlsanity.com/paper/1609.03675/) (2016)
- [TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation](https://mlsanity.com/paper/2005.02844/) (2020)
- [Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems](https://mlsanity.com/paper/1904.04381/) (2019)
- [Context-aware Sequential Recommendation](https://mlsanity.com/paper/1609.05787/) (2016)
- [Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks](https://mlsanity.com/paper/1906.04580/) (2019)
- [TIMERS: Error-Bounded SVD Restart on Dynamic Networks](https://mlsanity.com/paper/1711.09541/) (2017)
- [BasConv: Aggregating Heterogeneous Interactions for Basket Recommendation with Graph Convolutional Neural Network](https://mlsanity.com/paper/2001.09900/) (2020)
- [Personalized Graph Neural Networks with Attention Mechanism for Session-Aware Recommendation](https://mlsanity.com/paper/1910.08887/) (2019)

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

- [Towards Better Dynamic Graph Learning: New Architecture and Unified Library](https://mlsanity.com/paper/2303.13047/) (2023)
- [Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer](https://mlsanity.com/paper/2108.06625/) (2021)
- [Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer](https://mlsanity.com/paper/2105.00522/) (2021)
- [Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders](https://mlsanity.com/paper/2305.18612/) (2023)
- [Heterogeneous Similarity Graph Neural Network on Electronic Health Records](https://mlsanity.com/paper/2101.06800/) (2021)
- [A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges](https://mlsanity.com/paper/2405.00476/) (2024)
- [Knowledge Graph Completion Models are Few-shot Learners: An Empirical Study of Relation Labeling in E-commerce with LLMs](https://mlsanity.com/paper/2305.09858/) (2023)
- [Rank List Sensitivity of Recommender Systems to Interaction Perturbations](https://mlsanity.com/paper/2201.12686/) (2022)
- [GraphPro: Graph Pre-training and Prompt Learning for Recommendation](https://mlsanity.com/paper/2311.16716/) (2023)
- [SelfCF: A Simple Framework for Self-supervised Collaborative Filtering](https://mlsanity.com/paper/2107.03019/) (2021)
- [Dynamic Graph Representation Learning with Neural Networks: A Survey](https://mlsanity.com/paper/2304.05729/) (2023)
- [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)
- [HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers](https://mlsanity.com/paper/2311.18526/) (2023)
- [Co-Neighbor Encoding Schema: A Light-cost Structure Encoding Method for Dynamic Link Prediction](https://mlsanity.com/paper/2407.20871/) (2024)
- [Relation-aware Heterogeneous Graph for User Profiling](https://mlsanity.com/paper/2110.07181/) (2021)
- [Triple Modality Fusion: Aligning Visual, Textual, and Graph Data with Large Language Models for Multi-Behavior Recommendations](https://mlsanity.com/paper/2410.12228/) (2024)
- [Temporal Graph Signal Decomposition](https://mlsanity.com/paper/2106.13517/) (2021)
- [Deep Structural Point Process for Learning Temporal Interaction Networks](https://mlsanity.com/paper/2107.03573/) (2021)
- [Position-enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations](https://mlsanity.com/paper/2107.05235/) (2021)
- [Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation](https://mlsanity.com/paper/2109.11790/) (2021)
- [Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering](https://mlsanity.com/paper/2202.13140/) (2022)
- [Adaptive Dependency Learning Graph Neural Networks](https://mlsanity.com/paper/2312.03903/) (2023)
- [Adaptive Collaborative Filtering with Personalized Time Decay Functions for Financial Product Recommendation](https://mlsanity.com/paper/2308.01208/) (2023)
- [Time-aware Hyperbolic Graph Attention Network for Session-based Recommendation](https://mlsanity.com/paper/2301.03780/) (2023)
- [Beyond Spatio-Temporal Representations: Evolving Fourier Transform for Temporal Graphs](https://mlsanity.com/paper/2402.16078/) (2024)
- [A Survey on Deep Neural Networks in Collaborative Filtering Recommendation Systems](https://mlsanity.com/paper/2412.01378/) (2024)
- [FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuning](https://mlsanity.com/paper/2402.03481/) (2024)
- [Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders](https://mlsanity.com/paper/2211.09072/) (2022)
- [Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation](https://mlsanity.com/paper/2311.09577/) (2023)

## Abstract (arXiv)

Dynamic recommendation is essential for modern recommender systems to provide real-time predictions based on sequential data. In real-world scenarios, the popularity of items and interests of users change over time. Based on this assumption, many previous works focus on interaction sequences and learn evolutionary embeddings of users and items. However, we argue that sequence-based models are not able to capture collaborative information among users and items directly. Here we propose Dynamic Graph Collaborative Filtering (DGCF), a novel framework leveraging dynamic graphs to capture collaborative and sequential relations of both items and users at the same time. We propose three update mechanisms: zero-order 'inheritance', first-order 'propagation', and second-order 'aggregation', to represent the impact on a user or item when a new interaction occurs. Based on them, we update related user and item embeddings simultaneously when interactions occur in turn, and then use the latest embeddings to make recommendations. Extensive experiments conducted on three public datasets show that DGCF significantly outperforms the state-of-the-art dynamic recommendation methods up to 30. Our approach achieves higher performance when the dataset contains less action repetition, indicating the effectiveness of integrating dynamic collaborative information.