DIIT: A Domain-Invariant Information Transfer Method for Industrial Cross-Domain Recommendation

TL;DR

DIIT is a novel end-to-end method for industrial cross-domain recommendation that enhances effectiveness by extracting domain-invariant information and improves efficiency through targeted information transfer, validated on real and public datasets.

Contribution

The paper introduces DIIT, a new approach that simulates industrial environments and employs domain-invariant extractors and a migrator for effective and efficient cross-domain recommendation.

Findings

Effective on production and public datasets

Improves recommendation accuracy and efficiency

Outperforms existing CDR methods in industrial settings

Abstract

Cross-Domain Recommendation (CDR) have received widespread attention due to their ability to utilize rich information across domains. However, most existing CDR methods assume an ideal static condition that is not practical in industrial recommendation systems (RS). Therefore, simply applying existing CDR methods in the industrial RS environment may lead to low effectiveness and efficiency. To fill this gap, we propose DIIT, an end-to-end Domain-Invariant Information Transfer method for industrial cross-domain recommendation. Specifically, We first simulate the industrial RS environment that maintains respective models in multiple domains, each of them is trained in the incremental mode. Then, for improving the effectiveness, we design two extractors to fully extract domain-invariant information from the latest source domain models at the domain level and the representation level respectively. Finally, for improving the efficiency, we design a migrator to transfer the extracted information to the latest target domain model, which only need the target domain model for inference. Experiments conducted on one production dataset and two public datasets verify the effectiveness and efficiency of DIIT.