基于用户生成内容的用户需求预测CA-VAR-Markov模型
1School of Art and Design, Guilin University of Technology, Guilin, 541000, Guangxi, China.
Scientific reports
|March 5, 2025
概括
这项研究引入了一种新的方法,可以从社交媒体内容中预测用户需求,从而增强产品设计. 该方法准确地预测了不断变化的用户需求,帮助企业捕获市场.
科学领域:
- 数据科学数据科学数据科学
- 人工智能的人工智能
- 消费者行为分析 消费者行为分析
背景情况:
- 了解用户需求对于竞争环境中的市场成功至关重要.
- 社交媒体上的用户生成内容 (UGC) 为识别未满足的消费者需求提供了丰富的来源.
- 现有的方法可能缺乏准确性来捕捉用户偏好的动态变化.
研究的目的:
- 开发和验证一个强大的框架,用于从UGC预测用户需求.
- 通过准确预测消费者需求,增强产品设计策略.
- 为企业提供可操作的见解,以提高市场竞争力.
主要方法:
- 预处理UGC数据,包括除重复和停止词删除.
- 使用隐性迪里克莱特分配 (LDA) 进行特征提取和用户需求聚类.
- 采用从变压器 (BERT) 和长短期存储器 (LSTM) 的双向编码器表示来进行情绪分析和特征提取.
- 实施一个关联分析-向量自动回归-马尔科夫 (CA-VAR-Markov) 模型来预测用户需求的演变.
- 应用分析卡诺 (A-Kano) 模型用于产品设计优化策略.
主要成果:
- 与LSTM和ARIMA模型相比,拟议的方法在预测用户需求方面表现出更高的准确性.
- 使用"Autohome"UGC对NIO EC6的案例研究验证了预测框架的有效性.
- 综合方法成功地识别和预测用户需求趋势,为产品开发提供有价值的见解.
结论:
- 开发的方法提供了一种有效的手段来识别和预测用户从UGC的需求.
- 准确预测用户需求使企业能够优化产品设计并获得市场份额.
- 这项研究为寻求将产品与不断变化的消费者需求保持一致的企业提供了宝贵的参考资料.
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