Caching at The Edge: A Group Interest Aware Approach
Zhidu Li, Ruili Bao, Dapeng Wu, Honggang Wang, Ruyan Wang · 2021
How to improve the content caching efficiency and user coverage rate at the same time is a fundamental challenge in edge caching networks. This paper studies an edge caching scheme based on user interest to address this issue. Specifically, a group interest aware caching framework is first developed. An individual interest prediction scheme is then proposed by merging factorization machine (FM) model and multi-layer perceptron (MLP) model, where both low-order and high-order features can be well learned simultaneously. Thereafter, the group interest is represented by a weighted average approach, based on which a caching scheme is further proposed. Moreover, the effectiveness of the proposed method is validated by extensive experiments with a real-world dataset.