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.

翻译：为eBay等电子商务平台开发商品推荐系统面临独特挑战，主要源于数据稀疏性和用户兴趣多样性。尽管丰富的用户-商品交互至关重要，但eBay的数据稀疏程度比其他电商平台高出一个数量级。为应对这一挑战，我们提出CoActionGraphRec（CAGR）——一种基于文本的双塔深度学习模型（商品塔与用户塔），该模型利用协同作用图层。为增强用户和商品表征，我们采用专门针对eBay环境设计的图结构解决方案。在商品塔中，我们通过每个商品的协同作用商品构建协同作用图来捕捉协同信号，该图被图神经网络组件充分利用。在用户塔中，我们基于用户行为序列构建全连接图，其中边编码成对关系。此外，显式交互模块通过学习表征来捕捉行为交互。大量离线和在线A/B测试实验证明了所提方法的有效性，结果显示在关键指标上优于现有最先进方法。