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作 者:李宇远 杜杰 Li Yuyuan;Du Jie(China Southern Power Grid Digital Enterprise Technology(Guangdong)Co.,Ltd.China Southern Power Grid Digital Digital Grid Corporation Co.,Ltd.,Guangzhou 510670,China)
机构地区:[1]南方电网数字电网集团有限公司南方电网数字企业科技(广东)有限公司,广州510670
出 处:《兵工自动化》2025年第2期17-21,共5页Ordnance Industry Automation
摘 要:针对目前P2P平台贷款组合中传统推荐算法存在的缺点,提出一种基于多目标进化算法的组合贷款推荐模型。建立推荐组合预测评级和一致性目标函数,平衡推荐贷款组合的准确性和回报率;基于改进的多目标进化推荐算法对目标函数进行求解;为解决决策变量空间维数过高问题,提出决策空间降维和改进的初始化策略,加快种群的收敛速度,提升算法搜索效率。将所提模型分别与协同过滤(collaborative filtering,CF)、神经协同过滤(neural collaborative filtering,NCF)、物质扩散(probabilistic spreading,ProbS)、粒子群优化(particle swarm optimization,PSO)、动态规划算法(dynamic programming algorithm,DPA)、混合多目标进化和物质扩散算法(MOEA-Prob S)等模型进行对比,结果表明:所提模型性能有所提升,平均准确率为0.1047,平均利润系数为0.1542,平均风险系数为0.0023。In view of the shortcomings of the traditional recommendation algorithm in the loan portfolio of P2P platform,a loan portfolio recommendation model based on multi-objective evolutionary algorithm is proposed.To balance the accuracy and the rate of return of the recommended loan portfolio,the objective function is established to predict the rating and consistency of the recommended loan portfolio.The objective function is solved based on the improved multi-objective evolutionary recommendation algorithm.To solve the problem of high dimension of the decision variable space,the dimension reduction of the decision space and the improved initialization strategy are proposed to accelerate the convergence speed of the population and improve the search efficiency of the algorithm.The proposed model is compared with collaborative filtering(CF),neural collaborative filtering(NCF),probabilistic spreading(ProbS),particle swarm optimization(PSO),dynamic programming algorithm(DPA),MOEA-ProbS and other models,and the results show that the performance of the proposed model is improved,the average accuracy is 0.1047,the average profit coefficient is 0.1542,and the average risk coefficient is 0.0023.
分 类 号:TP393[自动化与计算机技术—计算机应用技术]
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