通过重构用户-物品图，高效预测互补商品，提升推荐准确率。
Revisiting Graph Projections for Effective Complementary Product Recommendation
- 基于有向加权图投影，挖掘历史交互中的互补关系
- 在多个基准上相比序列与图模型分别提升43%和38%
- 方法简单有效，适合电商场景的实时推荐系统
互补商品推荐是提升用户体验和零售销量的重要策略。然而，由于用户-物品交互数据噪声多、稀疏性强，精准推荐仍具挑战。本文提出一种简单而有效的方法，基于用户-物品二分图投影得到的有向加权图，预测给定查询商品的互补商品列表。我们重新审视推荐系统中的二分图投影机制，提出一种从历史交互中推断互补关系的新方法。在多个基准上，该模型虽结构简单，但仍相较最新序列推荐方法平均提升43%，相较图基方法平均提升38%。
原文摘要 · Abstract (English)
Complementary product recommendation is a powerful strategy to improve customer experience and retail sales. However, recommending the right product is not a simple task because of the noisy and sparse nature of user-item interactions. In this work, we propose a simple yet effective method to predict a list of complementary products given a query item, based on the structure of a directed weighted graph projected from the user-item bipartite graph. We revisit bipartite graph projections for recommender systems and propose a novel approach for inferring complementarity relationships from historical user-item interactions. We compare our model with recent methods from the literature and show, despite the simplicity of our approach, an average improvement of +43% and +38% over sequential and graph-based recommenders, respectively, over different benchmarks.
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