基于蚁群相似权算法的网络团购信用评价模型研究

Objective] To help online group-buying consumers find high quality merchants quickly and help merchants improve their credit efficiently. [Methods] Use similarity weight to distribute the weights of index system, consider the gotten composite indicator variables as the parameters of ant colony algorithm, and establish the credit evaluation model based on ACO and Similarity Weight Algorithm. [Results] Empirical results show that the model can effectively find out the shortest path to save time and money cost, obtain high quality merchant. [Limitations] Not considering the impact of special trade on online group-buying credit evaluation, such as refund and fictitious trading; directly using previous research conclusion of other parameters in ACO. [Conclusions] The results can help merchants improve credit, promote satisfaction of consumer group, and provide the references for further research on online group-buying problems.