Dynamic QoS Prediction via a Non-Negative Tensor Snowflake Factorization
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912346965803008 |
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| author | Xia, YongHui Wang, Lan Wu, Hao |
| author_facet | Xia, YongHui Wang, Lan Wu, Hao |
| contents | Dynamic quality of service (QoS) data exhibit rich temporal patterns in user-service interactions, which are crucial for a comprehensive understanding of user behavior and service conditions in Web service. As the number of users and services increases, there is a large amount of unobserved QoS data, which significantly affects users'choice of services. To predict unobserved QoS data, we propose a Non-negative Snowflake Factorization of tensors model. This method designs a snowflake core tensor to enhance the model's learning capability. Additionally, it employs a single latent factor-based, nonnegative multiplication update on tensor (SLF-NMUT) for parameter learning. Empirical results demonstrate that the proposed model more accurately learns dynamic user-service interaction patterns, thereby yielding improved predictions for missing QoS data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18588 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dynamic QoS Prediction via a Non-Negative Tensor Snowflake Factorization Xia, YongHui Wang, Lan Wu, Hao Machine Learning Artificial Intelligence Dynamic quality of service (QoS) data exhibit rich temporal patterns in user-service interactions, which are crucial for a comprehensive understanding of user behavior and service conditions in Web service. As the number of users and services increases, there is a large amount of unobserved QoS data, which significantly affects users'choice of services. To predict unobserved QoS data, we propose a Non-negative Snowflake Factorization of tensors model. This method designs a snowflake core tensor to enhance the model's learning capability. Additionally, it employs a single latent factor-based, nonnegative multiplication update on tensor (SLF-NMUT) for parameter learning. Empirical results demonstrate that the proposed model more accurately learns dynamic user-service interaction patterns, thereby yielding improved predictions for missing QoS data. |
| title | Dynamic QoS Prediction via a Non-Negative Tensor Snowflake Factorization |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2504.18588 |