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递归神经网络下混合属性信息推荐仿真
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作者 乔阳阳 刘楷正 +1 位作者 董涛 王丽娟 《计算机仿真》 2024年第6期544-548,共5页
信息量的大幅增加,导致用户无法从推荐的海量数据中提取到所需的信息。为了解决上述问题,提出一种基于递归神经网络的混合属性信息推荐算法。通过数据预处理方法,删除没有任何信息评分的混合属性信息,并挖掘用户和混合属性信息之间的关... 信息量的大幅增加,导致用户无法从推荐的海量数据中提取到所需的信息。为了解决上述问题,提出一种基于递归神经网络的混合属性信息推荐算法。通过数据预处理方法,删除没有任何信息评分的混合属性信息,并挖掘用户和混合属性信息之间的关系。采用已评分混合属性信息,融合极度梯度提升树(eXtreme Gradient Boosting, XGBoost)算法对混合属性信息分类。构建递归神经网络模型,采用梯度下降法对模型训练,获取用户对各个混合属性信息的概率值,并将其按照从大到小的顺序排列,形成推荐列表直接推送给用户完成推荐。实验结果表明,所提方法的HR值得到了提高,且NDCG取值的平均值为0.805,全面提升推荐结果的准确性。 展开更多
关键词 递归神经网络 混合属性信息 推荐算法 梯度下降
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Novel method for hybrid multiple attribute decision making based on TODIM method 被引量:1
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作者 Fang Wang Hua Li 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2015年第5期1023-1031,共9页
The TODIM(an acronym in Portuguese for interactive and multiple attribute decision making) method is a valuable tool to solve the multiple attribute decision making(MADM) problems considering the behavior of the decis... The TODIM(an acronym in Portuguese for interactive and multiple attribute decision making) method is a valuable tool to solve the multiple attribute decision making(MADM) problems considering the behavior of the decision maker(DM), while it cannot be used to handle the problem with unknown weight information on attributes. In this paper, a novel method based on the classical TODIM method is proposed to solve the hybrid MADM problems with unknown weight information on attributes,in which attribute values are represented in four different formats:crisp numbers, interval numbers, triangular fuzzy numbers and trapezoidal fuzzy numbers. Firstly, the positive-ideal alternative and negative-ideal alternative are determined, and the gain and loss matrices are constructed by calculating the gain and loss of each alternative relatived to the ideal alternatives concerning each attribute based on different distance calculation formulas, which may avoid the information missing or information distortion in the process of unifying multiform attribute values into a certain representation form. Secondly, an optimization model based on the maximizing deviation(MD) method, by which the attribute weights can be determined, is established for the TODIM method. Further, the calculation steps to solve the hybrid MADM problems are given. Finally, two numerical examples are presented to illustrate the usefulness of the proposed method, and the results show that the DM's psychological behavior, attribute weights and the transformed information would highly affect the ranking orders of alternatives. 展开更多
关键词 hybrid multiple attribute decision making TODIM RANKING
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