Summary of Web Service Qos Prediction Via Extended Canonical Polyadic-based Tensor Network, by Qu Wang et al.
Web Service QoS Prediction via Extended Canonical Polyadic-based Tensor Network
by Qu Wang, Hao Wu
First submitted to arxiv on: 29 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed Extended Canonical Polyadic-based Tensor Network (ECTN) model improves upon existing CP-based tensor network models by incorporating user-service correlation in predicting dynamic Quality of Service (QoS) values. By modeling this correlation via a relation dimension between user and service features in low-dimensional space, the ECTN enhances prediction accuracy compared to state-of-the-art QoS prediction models. The authors demonstrate the efficacy of their approach through experiments on two public dynamic QoS datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to predict how well different websites will perform based on how users interact with them. Right now, there are many websites that do similar things, and people want to choose the best one. To help make this choice easier, researchers have developed a special kind of math called Canonical Polyadic (CP) that helps predict how well these websites will work. However, this math doesn’t take into account how different users interact with different websites, which is important information. The new method, called ECTN, fixes this problem by adding an extra layer to the math that connects users and websites in a way that makes sense. This leads to better predictions about how well websites will work. |
