Collaborative Motivation Framework Leveraging Similar Interest Behavior in Semantic Web Applications

With the growth of web technology, the semantic web offers a promising framework for online knowledge collaboration. However, trust issues can undermine users' willingness to collaborate, reduce the frequency of interaction and collaboration efficiency. This paper introduces a super node-based trust management model designed to enhance semantic networks by linking nodes through trust relationships. The model exploits the synergistic incentives of similar interest behaviours to achieve a steady construction of trust relationships. We propose a similarity filtering algorithm that calculates the similarity to filter out false, misleading, or unfair information effectively. Through simulations, we compare our model with RRGRET, Surework, and community-based approaches, and the results show that our model has good network properties, while also resisting multiple malicious attacks and guaranteeing collaboration success. This research contributes to optimizing node relationships within semantic networks and strengthening network robustness against interference.