Attending to Customer Attention: A Novel Deep Learning Method for Leveraging Multimodal Online Reviews to Enhance Sales Prediction
Attending to Customer Attention: A Novel Deep Learning Method for Leveraging Multimodal Online Reviews to Enhance Sales Prediction
复制标题
关注客户注意力：一种利用多模式在线评论增强销售预测的新型深度学习方法
DOI：
10.1287/isre.2021.0292
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发表时间：
2023-07
影响因子：
4.9
通讯作者：
Gang Chen;Lihua Huang;S. Xiao;Chenghong Zhang;Huimin Zhao
中科院分区：
文献类型：
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作者：
Gang Chen;Lihua Huang;S. Xiao;Chenghong Zhang;Huimin Zhao
审查有用性通常依靠审查层面的量化指标来衡量。然而，由于评论信息质量、客户需求和产品属性的不断变化，由这些简单指标限定的有用评论不一定会产生准确的销售预测。假设具有较高的客户关注度的评论应该更有影响力的客户的购买意愿和产品销售，我们建议利用客户的注意力，以更好地实现销售预测的多模态评论的潜力。我们从整体评论集、评论子集、个体评论和评论元素四个层面对顾客注意力进行了概念化，归纳出四个顾客注意力指标，即及时性、语义多样性、投票意识和多模态交互。然后，我们提出了一种新的深度学习方法，该方法使用专门为基于多模态评论的销售预测设计的神经网络注意力机制来整合这些客户注意力指标。在预测酒店销售额（具体来说，月入住率）的案例研究中，基于大数据集的经验评估表明，在预测性能和表示学习性能方面，我们提出的方法优于基准的最先进的深度学习方法。随着多模态评论变得越来越普遍，这种方法可以作为充分利用这种多模态数据来支持业务决策的工具。
Review helpfulness has been measured commonly relying on quantitative indicators at the review level. Helpful reviews qualified by such simple indicators, however, may not necessarily yield accurate sales predictions, owing to the ever-evolving review information quality, customer demand, and product attributes. Positing that reviews with higher customer attention should be more influential to customers’ purchase intention and product sales, we propose to leverage customer attention to better realize the potential of multimodal reviews for sales prediction. We conceptualize customer attention at the holistic review set, review subset, individual review, and review element levels, respectively, and induce four indicators of customer attention, that is, timeliness, semantic diversity, voting-awareness, and varying multimodal interaction. We then propose a novel deep learning method, which incorporates these customer attention indicators using neural network attention mechanisms specifically designed for multimodal-review-based sales prediction. Empirical evaluation based on a large data set in a case study predicting hotel sales (specifically, monthly occupancy rate) shows that, in terms of both prediction performance and representation learning performance, our proposed method outperformed benchmarked state-of-the-art deep learning methods. As multimodal reviews become increasingly prevalent, this method serves as a tool for adequately leveraging such multimodal data to support business decision making.