A Trustworthy Service Selection Model Based on Collaborative Filtering

Current Quality of Service (QoS)-based service selection approaches pay little attention to personal properties and characteristics of service requesters. However, Collaborative Filtering (CF)-based service selection approaches fail to consider the trustworthiness of recommenders and can not resist malicious feedback from recommenders. This paper introduces user correlation to embody the impact of personal characteristics of service requesters on selection process, computes creditability of recommendation, and employs analytic hierarchy process to decide the weight of each factor in service reputation. With the integration of trust evaluation methods and CF techniques, a Trustworthy Services Selection Model based on Collaborative Filtering (TSSMCF) is presented. Simulation experiments demonstrate that TSSMCF model can not only improve the efficiency of service selection but also effectively avoid malicious attack from service recommenders.