Guosheng Kang

TL;DR: This paper proposes a network-aware Web service QoS prediction approach by integrating matrix factorization with the network map, and indicates that this approach outperforms previous MF and CF-based approaches in prediction accuracy.

TL;DR: By conducting large-scale experiments based on a real-world Web services dataset, it is shown that the AWSR system effectively recommends Web services based on users functional interests and non-functional requirements with excellent performance.

TL;DR: This paper proposes a novel web service recommendation approach incorporating a user's potential QoS preferences and diversity feature of user interests on web services, and presents an innovative diversity-aware web service ranking algorithm to rank theweb service candidates based on their scores, and diversity degrees derived from the web service graph.

TL;DR: In this paper, a global optimal service selection method for multiple related service requesters is proposed, which uses Euclidean distance with weights to measure degree of matching of services based on quality of service (QoS).

TL;DR: Theoretical analysis and experimental results indicate the feasibility and efficiency of this algorithm, and the execution efficiency and convergence rate of PSO-GODSS are much better than that of multi-objective genetic algorithm used in prior work.