基于蚁群相似权算法的网络团购信用评价模型研究

Study on Credit Evaluation Model of Online Group-buying by Using ACO and Similarity Weight Algorithm

【目的】帮助网络团购消费者快速找到优质商家,商家可以有效地提高自身信用水平。【方法】利用相似权测度法对指标体系分配权重,得出的综合指标变量作为蚁群算法参数,建立基于蚁群相似权的信用评价模型。【结果】实证研究表明,该模型能够快速有效地求出节约时间成本和货币成本的最短路径,找出优质商家。【局限】未考虑退款和刷单等特殊交易对网络团购信用评价的影响;对蚁群算法的其他参数未进行具体研究,直接采用前人研究结论。【结论】有助于商家提高信用、提升团体满意度,为进一步研究网络团购问题提供参考。

[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.