Quality of Service (QoS) prediction in service-oriented computing supports candidate service identification and resource scheduling. However, the limited single-source data dimension fails to capture dynamic characteristics such as network fluctuations, spatiotemporal migration, and user behavior coupling. This study integrates three types of heterogeneous information to construct a unified embedding space, achieves cross-source alignment of semantically heterogeneous data through a hierarchical architecture, introduces differentiated dynamic weight allocation among three data sources, captures nonlinear evolution patterns through temporal joint representations, and employs multi-source context-guided sparse compensation to address missing entries in the service invocation matrix. Experimental results demonstrate that the proposed method significantly outperforms single-source models in both prediction accuracy and robustness under complex scenarios.

Zhenzhen Liu. Modeling Method and Implementation Mechanism for QoS Prediction Based on Multi-Source Data Fusion. Academic Journal of Computing & Information Science (2026), Vol. 9, Issue 6: 9-14. https://doi.org/10.25236/AJCIS.2026.090602.

[1] Ren Lifang, Wang Wenjian. Method for QoS Prediction in Mobile Edge Computing Environment [J]. Journal of Chinese Computer Systems, 2020, 41 (6): 1176–1181.

[2] Chen Manman, Wang Junfeng, Li Xiaohui, et al. Temporal QoS Prediction Based on Multi-Source Features and Multitask Learning in Cloud Service Recommendation [J]. Journal of Sichuan University (Natural Science Edition), 2024, 61 (4): 140–150.

[3] Tan Hefei, Zong Rong, Wu Hao, et al. Multi-task Web Service QoS Prediction Based on Graph Convolution Network [J]. Computer Engineering and Design, 2024, 45 (1): 47–54.

[4] Xu Jianlong, Lin Jian, Li Yusen, et al. Distributed User Privacy Preserving Adjustable Personalized QoS Prediction Model for Cloud Services [J]. Chinese Journal of Network and Information Security, 2023, 9 (2): 70–80.

[5] Liu Jianxun, Ding Linghang, Kang Guosheng, et al. Joint QoS Prediction for Web Services Based on Deep Fusion of Features [J]. Journal on Communications, 2022, 43 (7): 215–226.