Generative Multi-Target Cross-Domain Recommendation

TL;DR

This paper introduces GMC, a generative multi-target cross-domain recommendation approach that uses semantic item identifiers and a sequence-to-sequence model to improve recommendation performance across multiple domains.

Contribution

The paper proposes a novel generative paradigm for MTCDR using semantic item identifiers and a domain-aware contrastive loss, addressing limitations of existing methods.

Findings

GMC outperforms baseline methods on five public datasets.

Semantic identifiers effectively unify multi-domain knowledge.

Domain-aware contrastive loss enhances recommendation accuracy.

Abstract

Recently, there has been a surge of interest in Multi-Target Cross-Domain Recommendation (MTCDR), which aims to enhance recommendation performance across multiple domains simultaneously. Existing MTCDR methods primarily rely on domain-shared entities (\eg users or items) to fuse and transfer cross-domain knowledge, which may be unavailable in non-overlapped recommendation scenarios. Some studies model user preferences and item features as domain-sharable semantic representations, which can be utilized to tackle the MTCDR task. Nevertheless, they often require extensive auxiliary data for pre-training. Developing more effective solutions for MTCDR remains an important area for further exploration. Inspired by recent advancements in generative recommendation, this paper introduces GMC, a generative paradigm-based approach for multi-target cross-domain recommendation. The core idea of GMC is to leverage semantically quantized discrete item identifiers as a medium for integrating multi-domain knowledge within a unified generative model. GMC first employs an item tokenizer to generate domain-shared semantic identifiers for each item, and then formulates item recommendation as a next-token generation task by training a domain-unified sequence-to-sequence model. To further leverage the domain information to enhance performance, we incorporate a domain-aware contrastive loss into the semantic identifier learning, and perform domain-specific fine-tuning on the unified recommender. Extensive experiments on five public datasets demonstrate the effectiveness of GMC compared to a range of baseline methods.