# Predictive Edge Caching through Deep Mining of Sequential Patterns in User Content Retrievals

arXiv: https://arxiv.org/abs/2210.02657 · HTML: https://mlsanity.com/paper/2210.02657/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 63 references that are in the map.
- First posted on arXiv 6 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Recurrent Neural Networks with Top-k Gains for Session-based Recommendations](https://mlsanity.com/paper/1706.03847/) (2017)
- [Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation](https://mlsanity.com/paper/1609.09152/) (2016)
- [Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-time Popularities](https://mlsanity.com/paper/1708.06698/) (2017)
- [A Reinforcement-Learning Approach to Proactive Caching in Wireless Networks](https://mlsanity.com/paper/1712.07084/) (2017)
- [Reinforcement Learning for Adaptive Caching with Dynamic Storage Pricing](https://mlsanity.com/paper/1812.08593/) (2018)
- [TEDGE-Caching: Transformer-based Edge Caching Towards 6G Networks](https://mlsanity.com/paper/2112.00633/) (2021)
- [Cocktail Edge Caching: Ride Dynamic Trends of Content Popularity with Ensemble Learning](https://mlsanity.com/paper/2101.05885/) (2021)
- [Living on the Edge: The Role of Proactive Caching in 5G Wireless Networks](https://mlsanity.com/paper/1405.5974/) (2014)
- [On the Complexity of Optimal Routing and Content Caching in Heterogeneous Networks](https://mlsanity.com/paper/1501.00216/) (2014)

## Built on by (most cited first, 3 of 3)

- [A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks](https://mlsanity.com/paper/2508.18803/) (2025)
- [Visual SLAM with 3D Gaussian Primitives and Depth Priors Enabling Novel View Synthesis](https://mlsanity.com/paper/2408.05635/) (2024)
- [Spatial Visibility and Temporal Dynamics: Revolutionizing Field of View Prediction in Adaptive Point Cloud Video Streaming](https://mlsanity.com/paper/2409.18236/) (2024)

## Abstract (arXiv)

Edge caching plays an increasingly important role in boosting user content retrieval performance while reducing redundant network traffic. The effectiveness of caching ultimately hinges on the accuracy of predicting content popularity in the near future. However, at the network edge, content popularity can be extremely dynamic due to diverse user content retrieval behaviors and the low-degree of user multiplexing. It's challenging for the traditional reactive caching systems to keep up with the dynamic content popularity patterns. In this paper, we propose a novel Predictive Edge Caching (PEC) system that predicts the future content popularity using fine-grained learning models that mine sequential patterns in user content retrieval behaviors, and opportunistically prefetches contents predicted to be popular in the near future using idle network bandwidth. Through extensive experiments driven by real content retrieval traces, we demonstrate that PEC can adapt to highly dynamic content popularity, and significantly improve cache hit ratio and reduce user content retrieval latency over the state-of-art caching policies. More broadly, our study demonstrates that edge caching performance can be boosted by deep mining of user content retrieval behaviors.