  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.
