GAN逆转和转移:向卖家推产品修改,以更好地提高用户偏好
Satyadwyoom Kumar1, Abhijith Sharma2, Apurva Narayan3
1Computer Science, Netaji Subhas Institute of Technology, New Delhi, Delhi, India.
PeerJ. Computer science
|February 3, 2025
概括
本研究引入了一种新的方法,通过根据客户交互来改进产品图像,为卖家产生审美反. 这种方法提高了用户偏好16.7%,改善了商品美学和卖家洞察力.
科学领域:
- 计算机科学 计算机科学
- 人与计算机的交互
- 人工智能的人工智能
背景情况:
- 推系统通常侧重于用户与项目的交互,以建议产品,增强客户体验.
- 现有的系统往往忽略了为卖家提供可操作的洞察力,关于物品的美学和市场吸引力.
- 卖家缺乏指导,以适应产品的视觉效果,以满足不断变化的,多样化的消费者偏好.
研究的目的:
- 通过推系统,提出一种新的方法,为使用推系统的卖家产生审美反.
- 为卖家提供精致的商品图像,反映集体客户偏好.
- 弥合以客户为中心的建议和以销售者为导向的产品开发之间的差距.
主要方法:
- 开发了一种方法,通过客户与物品的互动来生成精细的物品图像.
- 利用推系统算法从多个消费者偏好中学习.
- 设计实验来评估消费者数量和形象类对偏好分数的影响.
主要成果:
- 拟议的方法产生了鲜明和现实的图像.
- 与以前的方法相比,生成图像的用户偏好分数高出16.7%.
- 该系统有效地适应多个用户同时使用的动态偏好.
结论:
- 这种新的方法成功地为卖家提供了数据驱动的审美反.
- 精致的商品图像可以指导卖家优化产品吸引力,以获得更广泛的客户覆盖.
- 这项工作为利用推者系统提供了一个新的方向,超越了简单的产品建议.
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