@article{He2026, 
author = {Fugui He and Huiying Liu and Yiwen Zhang},
title = {Bilateral Personalized Quality Centric Service Recommendation},
year = {2026},
journal = {Tsinghua Science and Technology},
volume = {31},
number = {5},
pages = {2381-2398},
keywords = {service recommendation, quality, dynamic skyline (DSL), reverse skyline (RSL), k-nearest neighbor (KNN)},
url = {https://www.sciopen.com/article/10.26599/TST.2024.9010240},
doi = {10.26599/TST.2024.9010240},
abstract = {The widespread adoption of service-oriented architecture in software engineering has fueled the rapid growth of web and cloud services, as well as service-based systems. With the proliferation of numerous functionally-equivalent services, each offering varying quality levels, finding the appropriate service has become increasingly challenging and essential. This challenge has made service recommendation a critical area of research and practical interest. However, existing methods, such as those relying on utility functions or skyline techniques, failed to address a fundamental issue: recommending services that align with users’ specific quality preferences, such as response time or failure rate. This problem involves two main aspects: (1) identifying appropriate services for user requests, and (2) identifying suitable users for new services. This paper proposes a set of approaches for bilateral personalized quality centric service recommendation, integrating k-nearest neighbors, dynamic skyline, and reverse dynamic skyline techniques. Our methods address the shortcomings of existing solutions by identifying both qualified and representative services and users. Extensive experiments on a dataset of 2507 real-world web services validate the effectiveness and efficiency of our approaches.}
}