In large-scale e-commerce platforms like Taobao, it is a big challenge to retrieve products that satisfy users from billions of candidates. This has been a common concern of academia and industry. Recently, plenty of works in this domain have achieved significant improvements by enhancing embedding-based retrieval (EBR) methods, including the Multi-Grained Deep Semantic Product Retrieval (MGDSPR) model [16] in Taobao search engine. However, we find that MGDSPR still has problems of poor relevance and weak personalization compared to other retrieval methods in our online system, such as lexical matching and collaborative filtering. These problems promote us to further strengthen the capabilities of our EBR model in both relevance estimation and personalized retrieval. In this paper, we propose a novel Multi-Objective Personalized Product Retrieval (MOPPR) model with four hierarchical optimization objectives: relevance, exposure, click and purchase. We construct entire-space multi-positive samples to train MOPPR, rather than the single-positive samples for existing EBR models.We adopt a modified softmax loss for optimizing multiple objectives. Results of extensive offline and online experiments show that MOPPR outperforms the baseline MGDSPR on evaluation metrics of relevance estimation and personalized retrieval. MOPPR achieves 0.96% transaction and 1.29% GMV improvements in a 28-day online A/B test. Since the Double-11 shopping festival of 2021, MOPPR has been fully deployed in mobile Taobao search, replacing the previous MGDSPR. Finally, we discuss several advanced topics of our deeper explorations on multi-objective retrieval and ranking to contribute to the community.
翻译:在如道保这样的大型电子商务平台中,检索令数十亿候选人的用户满意的产品是一项巨大的挑战,这是学术界和工业界共同关注的一个共同问题。最近,这一领域的大量工作通过加强嵌入式检索(EBR)方法取得了显著改进,包括道保搜索引擎中的多感性深度产品检索(MGDSPR)模型[16]。然而,我们发现,与在线系统的其他检索方法相比,MGDSPR仍然存在相关性差和人性化薄弱的问题,如词汇匹配和协作过滤。这些问题促使我们进一步加强了EBR模型在相关性估计和个性化检索两方面的能力。在本文件中,我们提出了一个新的多感性性化个人化产品检索(MOPR)模型(MDPR)模型(MDMDS)(多层次优化:相关性、接触、点击和购买。我们为培训MOPRPO的双向性更深度样本(MOPR),而不是现有ER模型的单一性能性能样本。我们采用经修改的软性减性损失来优化多种目标。自IMPRMR的大规模离线和在线交易模型测试以来,已经实现了IMPRDRMDR的升级的升级。