用积分指标统一优化推荐算法多维度表现
Theoretical foundations of the integral indicator application in hyperparametric optimization
- 提出积分评估法，将准确率、排序质量等指标融合为单一目标
- 实现精度、多样性与资源消耗的平衡，优于单一指标优化
- 适用于推荐系统及广泛机器学习任务，具通用性
本文探讨了基于积分评估的超参数优化方法在推荐算法中的应用，该方法将准确率、排序质量、输出多样性及算法资源消耗等多个性能指标整合为单一综合准则。相比传统仅依赖单一指标的优化方式，该方法能更均衡地提升系统整体表现。研究的理论意义在于构建了一种通用的多目标优化工具，不仅适用于推荐系统，还可推广至广泛的机器学习与数据分析场景。
原文摘要 · Abstract (English)
The article discusses the concept of hyperparametric optimization of recommendation algorithms using an integral assessment that combines various performance indicators into a single consolidated criterion. This approach is opposed to traditional methods of setting up a single metric and allows you to achieve a balance between accuracy, ranking quality, variety of output and the resource intensity of algorithms. The theoretical significance of the research lies in the development of a universal multi-criteria optimization tool that is applicable not only in recommendation systems, but also in a wide range of machine learning and data analysis tasks.
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