用共行为图建模用户与商品交互，提升电商稀疏数据下的推荐效果。
CoActionGraphRec: Sequential Multi-Interest Recommendations Using Co-Action Graphs
- 构建共行为图，捕捉商品间协同信号，增强物品表征。
- 用户行为序列建图，通过边编码行为间关系，学习动态兴趣。
- 在eBay真实数据上表现优于主流方法，适合高稀疏场景。
eBay等电商平台面临数据稀疏和用户兴趣多样化的挑战，其数据稀疏程度比其他平台高一个数量级。为应对该问题，本文提出CoActionGraphRec（CAGR），一种基于文本的双塔深度学习模型（物品塔与用户塔），结合共行为图层。物品塔通过共行为图中关联商品，利用图神经网络捕获协同信号；用户塔将用户行为序列构建为全连接图，边编码行为对之间的关系，并引入显式交互模块学习行为交互表示。大量离线与在线A/B测试表明，该方法在关键指标上优于现有最优模型。
原文摘要 · Abstract (English)
There are unique challenges to developing item recommender systems for e-commerce platforms like eBay due to sparse data and diverse user interests. While rich user-item interactions are important, eBay's data sparsity exceeds other e-commerce sites by an order of magnitude. To address this challenge, we propose CoActionGraphRec (CAGR), a text based two-tower deep learning model (Item Tower and User Tower) utilizing co-action graph layers. In order to enhance user and item representations, a graph-based solution tailored to eBay's environment is utilized. For the Item Tower, we represent each item using its co-action items to capture collaborative signals in a co-action graph that is fully leveraged by the graph neural network component. For the User Tower, we build a fully connected graph of each user's behavior sequence, with edges encoding pairwise relationships. Furthermore, an explicit interaction module learns representations capturing behavior interactions. Extensive offline and online A/B test experiments demonstrate the effectiveness of our proposed approach and results show improved performance over state-of-the-art methods on key metrics.
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