Biblio

In order to improve the accuracy of similarity, an improved collaborative filtering algorithm based on trust and information entropy is proposed in this paper. Firstly, the direct trust between the users is determined by the user's rating to explore the potential trust relationship of the users. The time decay function is introduced to realize the dynamic portrayal of the user's interest decays over time. Secondly, the direct trust and the indirect trust are combined to obtain the overall trust which is weighted with the Pearson similarity to obtain the trust similarity. Then, the information entropy theory is introduced to calculate the similarity based on weighted information entropy. At last, the trust similarity and the similarity based on weighted information entropy are weighted to obtain the similarity combing trust and information entropy which is used to predicted the rating of the target user and create the recommendation. The simulation shows that the improved algorithm has a higher accuracy of recommendation and can provide more accurate and reliable recommendation service.

This paper proposes a service operator-aware trust scheme (SOTS) for resource matchmaking across multiple clouds. Through analyzing the built-in relationship between the users, the broker, and the service resources, this paper proposes a middleware framework of trust management that can effectively reduces user burden and improve system dependability. Based on multidimensional resource service operators, we model the problem of trust evaluation as a process of multi-attribute decision-making, and develop an adaptive trust evaluation approach based on information entropy theory. This adaptive approach can overcome the limitations of traditional trust schemes, whereby the trusted operators are weighted manually or subjectively. As a result, using SOTS, the broker can efficiently and accurately prepare the most trusted resources in advance, and thus provide more dependable resources to users. Our experiments yield interesting and meaningful observations that can facilitate the effective utilization of SOTS in a large-scale multi-cloud environment.