Enhancing trustworthiness evaluation in internetware with similarity and non-negative constraints

Internetware is envisioned as a new software paradigm where software developers usually need to interact with unknown partners as well as the software entities developed by them. To reduce uncertainty and boost collaborations in such setting, it is important to provide trustworthiness evaluation mechanisms so that trustworthy partners/entities can be easily found. In this work, we propose a novel trustworthiness evaluation mechanism by enhancing existing mechanisms with similarity and non-negative constraints. To be specific, we first extend an existing multi-aspect trust inference model by incorporating the non-negative constraint. One of the advantages of such constraint is its strong interpretability. Second, we incorporate similarity into two neighborhood models borrowed from recommender systems. When computing similarity, we make use of the intermediate results from the first step. Finally, these models are combined under a machine learning framework. To show the effectiveness of our method, we conduct experiments on a real data-set. The results show that: both our non-negativity extension and similarity computation improve the evaluation accuracy of the original methods, and the combined method outperforms several state-of-the-art methods.