Motif-Based Prompt Learning for Universal Cross-Domain Recommendation

TL;DR

This paper introduces MOP, a motif-based prompt learning framework for universal cross-domain recommendation, which captures structural domain knowledge through motifs and improves transferability across diverse recommendation scenarios.

Contribution

The paper proposes a novel motif-based shared embedding approach and a unified pre-training and prompt tuning paradigm for cross-domain recommendation, enhancing adaptability and transferability.

Findings

MOP outperforms state-of-the-art models on four CDR tasks.

Motif-based embeddings effectively capture structural domain knowledge.

Unified pre-training and prompt tuning improve transfer learning in CDR.

Abstract

Cross-Domain Recommendation (CDR) stands as a pivotal technology addressing issues of data sparsity and cold start by transferring general knowledge from the source to the target domain. However, existing CDR models suffer limitations in adaptability across various scenarios due to their inherent complexity. To tackle this challenge, recent advancements introduce universal CDR models that leverage shared embeddings to capture general knowledge across domains and transfer it through "Multi-task Learning" or "Pre-train, Fine-tune" paradigms. However, these models often overlook the broader structural topology that spans domains and fail to align training objectives, potentially leading to negative transfer. To address these issues, we propose a motif-based prompt learning framework, MOP, which introduces motif-based shared embeddings to encapsulate generalized domain knowledge, catering to both intra-domain and inter-domain CDR tasks. Specifically, we devise three typical motifs: butterfly, triangle, and random walk, and encode them through a Motif-based Encoder to obtain motif-based shared embeddings. Moreover, we train MOP under the "Pre-training \& Prompt Tuning" paradigm. By unifying pre-training and recommendation tasks as a common motif-based similarity learning task and integrating adaptable prompt parameters to guide the model in downstream recommendation tasks, MOP excels in transferring domain knowledge effectively. Experimental results on four distinct CDR tasks demonstrate the effectiveness of MOP than the state-of-the-art models.