QoS-Centric Diversified Web Service Recommendation Based on Personalized Determinantal Point Process

With the popularity and widespread adoption of SOA (Service-Oriented Architecture), number Web services has increased exponentially. Users tend to use online for their daily business software development needs. large service candidates, recommending desirable that meet users’ personalized QoS (Quality Service) requirements becomes a challenging research issue, as preference is usually difficult satisfy users, i.e., uncertain. To solve this problem, some recent works have aimed recommend QoS-diversified enhance probability fulfilling user’s latent preferences. However, existing recommendation methods with uniform diversity degree different while are not considered. end, paper proposes mine from invocation history provides algorithm, named PDPP (Personalized Determinantal Point Process), through which list preferred generated user. Comprehensive experimental results show proposed approach can provide diversified ensuring overall accuracy results.