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A Geo-Aware Personalized Network for User and Service Representation and Bilinear Interaction Modeling in QoS Prediction
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DOI：10.1109/tnsm.2026.3719697.png)
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Abstract
En 中文
With the rapid growth of the Internet, the proliferation of functionally similar web services has made Quality of Service (QoS) prediction, which measures service performance, increasingly critical. In QoS prediction, the QoS values observed from user-service invocations are often significantly affected by their geographical location. However, existing QoS prediction methods typically assume static user and service representations, overlooking geographic differences. We argue that even the same user or service should have personalized representations based on different geographic locations. To address this, we propose GeoPerNet, a Geo-Aware Personalized Network for QoS Prediction. Specifically, we design the Geographical Aware Personalization Module, which models the geographical similarity between users and services to select the most relevant top-k neighbors for the target user or service. We then apply geographic similarity-based weighting to highlight key neighbor information. Next, we leverage the designed GeoTransformer to model the complex dependency relationships among neighbors. Finally, the refined neighbor representations are fused with the original embeddings to generate personalized user and service representations. Additionally, we design the Bilinear Interaction Module to capture fine-grained interaction relationships between users and services using a bilinear function. Experiments on the large-scale WS-DREAM dataset demonstrate that GeoPerNet outperforms state-of-the-art approaches.
Keywords:
QoS prediction
personalized representation learning
bilinear interaction
deep learning
service recommendation
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