Summary of Gacl: Graph Attention Collaborative Learning For Temporal Qos Prediction, by Shengxiang Hu et al.
GACL: Graph Attention Collaborative Learning for Temporal QoS Prediction
by Shengxiang Hu, Guobing Zou, Bofeng Zhang, Shaogang Wu, Shiyi Lin, Yanglan Gan, Yixin Chen
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses a crucial problem in dynamic service-oriented environments: accurately predicting Quality of Service (QoS) over time. Current methods neglect high-order relationships between users and services, fail to adjust feature learning for specific invocations, and struggle with modeling long-term QoS trends using Recurrent Neural Networks (RNNs). To overcome these limitations, the authors propose Graph Attention Collaborative Learning (GACL), a novel framework that incorporates a dynamic user-service graph, target-prompt graph attention network, and multi-layer Transformer encoder. GACL extracts deep latent features from users and services at each time slice, considering historical interactions and QoS values. Experimental results on the WS-DREAM dataset show that GACL outperforms state-of-the-art methods by up to 38.80% across multiple evaluation metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to predict how well a service will work tomorrow based on what happened yesterday and earlier today. This is an important problem, because if the service doesn’t work well, users might get upset. Right now, there are some ways to try to solve this problem, but they don’t always work very well. They neglect important relationships between users and services, and they struggle to see long-term patterns. The authors of this paper propose a new way to do this called Graph Attention Collaborative Learning (GACL). It uses special kinds of computer programs called graphs to understand how users and services interact over time. GACL is able to make better predictions than other methods because it takes into account these important relationships and long-term patterns. |
Keywords
» Artificial intelligence » Attention » Encoder » Graph attention network » Prompt » Transformer
