Graph Contrastive Learning with Multi-Objective for Personalized Product Retrieval in Taobao Search

TL;DR

This paper introduces GCL-MO, a graph contrastive learning model that enhances personalized product retrieval in Taobao search by effectively modeling collaborative signals and user behaviors, leading to improved retrieval accuracy and robustness.

Contribution

The paper proposes a novel multi-objective graph contrastive learning framework that incorporates multiple user behaviors and relevance constraints for better personalization in e-commerce search.

Findings

GCL-MO outperforms baseline in offline and online metrics.

Significant improvement observed in online A/B testing.

Enhanced long-tail item representation robustness.

Abstract

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.