Your decision path does matter in pre-training industrial recommenders with multi-source behaviors

TL;DR

This paper introduces HIER, a hierarchical representation learning method that incorporates users' decision paths in cross-domain recommendation, leveraging graph neural networks and contrastive learning to improve content delivery.

Contribution

It proposes a novel approach that models user decision paths and integrates high-order topological information for enhanced cross-domain recommendation.

Findings

HIER outperforms existing methods in online and offline tests.

The use of decision path modeling improves recommendation accuracy.

Graph neural networks effectively capture multi-source behavior relationships.

Abstract

Online service platforms offering a wide range of services through miniapps have become crucial for users who visit these platforms with clear intentions to find services they are interested in. Aiming at effective content delivery, cross-domain recommendation are introduced to learn high-quality representations by transferring behaviors from data-rich scenarios. However, these methods overlook the impact of the decision path that users take when conduct behaviors, that is, users ultimately exhibit different behaviors based on various intents. To this end, we propose HIER, a novel Hierarchical decIsion path Enhanced Representation learning for cross-domain recommendation. With the help of graph neural networks for high-order topological information of the knowledge graph between multi-source behaviors, we further adaptively learn decision paths through well-designed exemplar-level and information bottleneck based contrastive learning. Extensive experiments in online and offline environments show the superiority of HIER.