A Feature Distribution Smoothing Network Based on Gaussian Distribution for QoS Prediction

Lu, Tongxin; Zhang, Xiaohong; Wang, Ziliang; Yan, Meng

doi:10.1109/icws60048.2023.00087

Cited by 3 publications

(1 citation statement)

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“…Zhang et al [35] introduced a feature mapping and inference network method to capture the deep features of users and services and made up for the loss of feature information with feature compensation blocks. Lu et al [36] investigated a Gaussian feature distribution smoothing network to address the issues of noise and label imbalance in the dataset for QoS prediction.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Location-Aware Deep Interaction Forest for Web Service QoS Prediction

Zhu,

Ding,

Yang

2024

Applied Sciences

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With the rapid development of the web service market, the number of web services shows explosive growth. QoS is an important factor in the recommendation scene; how to accurately recommend a high-quality service for users among the massive number of web services has become a tough problem. Previous methods usually acquired feature interaction information by network structures like DNN to improve the QoS prediction accuracy, but this generates unnecessary computations. Aiming at addressing the above problem, inspired by the multigrained scanning mechanism in a deep forest, we propose a location-aware deep interaction forest approach for web service QoS prediction (LDIF). This approach offers the following innovations: The model fuses the location similarity of users and services as a latent feature representation of them. In addition, we designed a scanning interaction structure (SIS), which obtains multiple local feature combinations from the interaction between user and service features, uses interactive computing to extract feature interaction information, and concatenates the feature interaction information with original features, which aims to enhance the dimension of the features. Equipped with these, we compose a layer-by-layer cascade by using SIS to fuse low- and high-order feature interaction information, and the early-stop mechanism controls the cascade depth to avoid unnecessary computation. The experiments demonstrate that our model outperforms eight other state-of-the-art methods on MAE and RMSE common metrics on real public datasets.

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Section: Deep Learning Methodsmentioning

confidence: 99%