使用偏好诱导和Choquet积分的个性化捆绑建议
Erich Robbi1, Marco Bronzini1,2, Paolo Viappiani3
1Department of Information Engineering and Computer Science, University of Trento, Trento, Italy.
Frontiers in artificial intelligence
|February 29, 2024
概括
这项研究引入了一种新的捆绑推方法,使用Choquet整体来模拟属性协同作用,改进环境友好性建议. 实验表明,这种方法优于当地食品的标准方法.
科学领域:
- 推系统是一个推系统.
- 决策理论 决策理论
- 计算可持续性的计算可持续性
背景情况:
- 捆绑推旨在分组相关产品,但由于属性相互依赖,建模用户实用是复杂的.
- 现有的方法往往无法捕捉产品属性之间的协同效应,影响推质量.
研究的目的:
- 引入一种新的基于偏好的捆绑推方法,使用Choquet积分.
- 为了使属性联盟的偏好正式化,使得能够考虑属性协同作用的建议.
- 评估Choquet整体在改善推产品包的环保性方面的有效性.
主要方法:
- 开发了一个基于偏好的捆绑推框架,采用Choquet积分.
- 对环境属性的联盟进行正式的偏好,以捕捉相互依赖.
- 通过使用意大利北部当地食品数据集进行实验评估.
- 研究了偏好诱导策略,以从用户反中获得Choquet积分权重.
主要成果:
- 巧克特集成有效地将环保友好性正式化为捆绑建议.
- 标准加权总和方法推包与较低的环境友好,即使优化为它.
- 拟议的方法在推环保产品包方面表现出卓越的表现.
- 偏好诱导策略成功获得了必要的权重,使得最优的捆绑建议能够在最小的用户交互下获得最佳的建议.
结论:
- 巧克特积分为捆绑推提供了一个强大的方法,特别是在捕捉属性协同作用和环境考虑方面.
- 与传统方法相比,这种方法提高了建议的准确性和相关性.
- 有效的偏好诱导可以显著提高捆绑推系统的个性化和效率.
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