In e-commerce search, personalized retrieval is a crucial technique for improving user shopping experience. Recent works in this domain have achieved significant improvements by the representation learning paradigm, e.g., embedding-based retrieval (EBR) and collaborative filtering (CF). EBR methods do not sufficiently exploit the useful collaborative signal and are difficult to learn the representations of long-tail item well. Graph-based CF methods improve personalization by modeling collaborative signal within the user click graph. However, existing Graph-based methods ignore user's multiple behaviours, such as click/purchase and the relevance constraint between user behaviours and items.In this paper, we propose a Graph Contrastive Learning with Multi-Objective (GCL-MO) collaborative filtering model, which solves the problems of weak relevance and incomplete personalization in e-commerce search. Specifically, GCL-MO builds a homogeneous graph of items and then optimizes a multi-objective function of personalization and relevance. Moreover, we propose a modified contrastive loss for multi-objectives graph learning, which avoids the mutual suppression among positive samples and thus improves the generalization and robustness of long-tail item representations. These learned item embeddings are then used for personalized retrieval by constructing an efficient offline-to-online inverted table. GCL-MO outperforms the online collaborative filtering baseline in both offline/online experimental metrics and shows a significant improvement in the online A/B testing of Taobao search.

翻译：在电商搜索中，个性化检索是提升用户体验的关键技术。该领域近期工作通过表示学习范式（如基于嵌入的检索（EBR）和协同过滤（CF））取得了显著进展。然而，EBR方法未能充分利用有效的协同信号，且难以学习长尾商品的表示。基于图的CF方法通过建模用户点击图中的协同信号增强个性化，但现有图方法忽视了用户的多行为（如点击/购买）以及用户行为与商品间的相关性约束。本文提出多目标图对比学习（GCL-MO）协同过滤模型，解决了电商搜索中弱相关性与个性化不充分的问题。具体而言，GCL-MO构建商品同构图并优化个性化和相关性的多目标函数。此外，我们提出一种改进的对比损失用于多目标图学习，该损失避免了正样本间的相互抑制，从而提升长尾商品表示的泛化性和鲁棒性。学到的商品嵌入通过构建高效的离线到在线倒排索引用于个性化检索。GCL-MO在离线/在线实验指标上均优于线上协同过滤基线，并在淘宝搜索的在线A/B测试中显示出显著提升。