An Integrated-Model QoS-Based Graph for Web Service Recommendation

Web services (WS) are integrated software components that facilitate interoperable machine-to-machine interaction over a network. In the era of Web 2.0, companies worldwide are actively deploying Web services within their business environments. As a result, designing effective Web service recommendation mechanisms based on Quality of Service (QoS) is attracting more attention. However, traditional Neighborhood-based Collaborative Filtering (CF) models fail to capture the actual relationships between users or services due to data sparsity. On the other hand, Random Walk (RW) algorithm, which has been categorized as a sparsity-tolerant recommendation approach, suffers from poor performance in terms of recommendation accuracy. In this paper, we aim at designing a recommendation model that achieves high recommendation accuracy over the transitional RW based model. First, we propose an Integrated-Model QoS-based Graph (IMQG), in which users and services represent the nodes while weighted QoS magnitudes and User/Service similarity measurements serve as the edges. We use Jaccard coefficient in several variants to separately compute similarities of both Users and Services. Then, Top-k Random Walk algorithm is applied to generate final recommendation list to active users. Finally, to demonstrate the effectiveness of our model, comprehensive experiments are conducted on a real-world QoS dataset. Analysis of the results shows high improvement in recommendation accuracy with more tolerance to data sparsity.