  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.
