CATS: Clustering-Aggregated and Time Series for Business Customer Purchase Intention Prediction
CATS：用于客户购买意向预测的聚类聚合与时间序列方法AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确将再次购买预测视为时间序列预测，并以 GRU 提取购买序列特征，因此选择时间序列方向；应用场景是客户行为。
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CATS：用于客户购买意向预测的聚类聚合与时间序列方法
- 研究问题
- 在偶尔购买者数量多、忠诚客户数量少的情况下，如何结合客户特征和购买历史，预测客户是否会再次购买并处理群体不平衡？
- 方法与思路
- 作者提出 CAGRU：先用客户画像聚类，再按群组分别训练 GRU，以注意力机制提取重要序列位置，并结合多模态数据预测。
- 材料陈述的贡献
- 摘要称，在构建的四个数据集上进行了广泛实验，支持所提方法的预测优势；其设计重点是结合群组差异与组内共同特征。
- 阅读关注点
- 应核对再次购买的预测窗口、聚类与训练的数据划分、少数群组评价指标，以及四个数据集和比较方法能否支持摘要中的优势表述。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收均未核实；摘要未说明数据集细节、评价指标及优势幅度，不能判断预测的外部适用性。
原文摘要
Accurately predicting customers' purchase intentions is critical to the success of a business strategy. Current researches mainly focus on analyzing the specific types of products that customers are likely to purchase in the future, little attention has been paid to the critical factor of whether customers will engage in repurchase behavior. Predicting whether a customer will make the next purchase is a classic time series forecasting task. However, in real-world purchasing behavior, customer groups typically exhibit imbalance - i.e., there are a large number of occasional buyers and a small number of loyal customers. This head-to-tail distribution makes traditional time series forecasting methods face certain limitations when dealing with such problems. To address the above challenges, this paper proposes a unified Clustering and Attention mechanism GRU model (CAGRU) that leverages multi-modal data for customer purchase intention prediction. The framework first performs customer profiling with respect to the customer characteristics and clusters the customers to delineate the different customer clusters that contain similar features. Then, the time series features of different customer clusters are extracted by GRU neural network and an attention mechanism is introduced to capture the significance of sequence locations. Furthermore, to mitigate the head-to-tail distribution of customer segments, we train the model separately for each customer segment, to adapt and capture more accurately the differences in behavioral characteristics between different customer segments, as well as the similar characteristics of the customers within the same customer segment. We constructed four datasets and conducted extensive experiments to demonstrate the superiority of the proposed CAGRU approach.