Bilateral Personalized Quality Centric Service Recommendation

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.