SaaS Service Combinatorial Trustworthiness Measurement Method Based on Markov Theory and Cosine Similarity

Table 7

RTRM method.

RTRM method	Descriptions
Ease of use	It needs not only the trust relationship of the services directly associated with the tested service but also the information relationship of the indirectly associated services and the trust relationship between the indirectly associated services. The information relationship data are not easy to obtain, and the number of indirect recommenders is not easy to determine; unable to recommend new services for scenes where the complete recommender cannot be obtained. Therefore, the stability of the result is not high.
Objectivity	The objectivity of this method is mainly reflected in the input data. The trust relationship and recommendation trust relationship are calculated from the log data. Therefore, it has strong objectivity.
Versatility	This method is only available for scenarios where it is easy to obtain the directly and indirectly related service trust relationship of the tested composition. Therefore, its universality is not strong.
Functionality	This method has a single function and can only obtain the trustworthiness results of one service pair at a time.
Costs	The time cost of this method is large in the early stage. When the trust relationship and trust value between each service are obtained, the cost is reduced.