Dynamic trust evaluation model based on bidding and multi-attributes for social networks

Mutual trust is the most important basis in social networks. However, many malicious nodes often deceive, collaboratively cheat, and maliciously recommend other nodes for getting the more benefits. Meanwhile, because of lacking effective incentive strategy, many nodes are neither to evaluate nor to recommend. Thus, malicious actions have been aggravated in social networks. To solve these issues, we designed a bidding strategy to incentivise nodes to do their best to recommend or evaluate service node. At the same time, we also employed TOPSIS method of selecting a correct service node for system from networks. To guarantee reliability of service node selected, we brought recommendation time influential function, service content similarity function and recommendation acquaintance function into the model to compute general trust of node. Finally, we gave an update method for trust degree of node and experiments analysis.