Beyond Complements and Substitutes: A Graph Neural Network Approach for Collaborative Retail Sales Forecasting
提出一种利用产品间多重关系（基于跨品类选择依赖理论）的图神经网络方法，提升零售销售预测准确性，为库存、采购、定价和促销等决策提供数据支持。
Practice-Oriented Abstract This paper proposes a novel approach for enhanced sales forecasting by leveraging multifaceted product relations, disentangled on the ground of the cross-category choice dependence theory. With superior forecasting performance over state-of-the-art alternatives and a deep understanding of product relations, the proposed approach has significant practical implications for various stakeholders (e.g., retail store managers, inventory department, purchasing department, operational staff, marketers, and retail platforms). On the one hand, improved forecasting could provide solid data-driven decision support for supply chain management, resource planning, inventory control, and purchasing planning. The semblance of predictive power in sales forecasting demonstrates operational utility. On the other hand, derived insights on product relations could facilitate reasonable pricing and promotion strategies, enhance the relevance and diversity of recommendation systems, and provide benefits for assortment planning, cross-selling, and shelf space allocation.