# Dynamic QoS Prediction via a Non-Negative Tensor Snowflake Factorization

arXiv: https://arxiv.org/abs/2504.18588 · HTML: https://mlsanity.com/paper/2504.18588/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 56 references that are in the map.
- First posted on arXiv 24 Apr 2025.

## Builds on (most cited first, 2 of 2)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)

## Abstract (arXiv)

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.