Coherence-guided Preference Disentanglement for Cross-domain Recommendations

TL;DR

This paper introduces CoPD, a novel method for cross-domain recommendation that leverages shared item attributes and preference disentanglement to improve recommendation accuracy despite limited shared user data.

Contribution

The paper proposes a coherence-guided preference disentanglement approach that explicitly extracts shared item attributes and disentangles user preferences for better cross-domain recommendations.

Findings

CoPD outperforms existing baselines in real-world datasets.

Shared item attributes effectively guide preference learning.

Disentangling preferences improves recommendation accuracy.

Abstract

Discovering user preferences across different domains is pivotal in cross-domain recommendation systems, particularly when platforms lack comprehensive user-item interactive data. The limited presence of shared users often hampers the effective modeling of common preferences. While leveraging shared items' attributes, such as category and popularity, can enhance cross-domain recommendation performance, the scarcity of shared items between domains has limited research in this area. To address this, we propose a Coherence-guided Preference Disentanglement (CoPD) method aimed at improving cross-domain recommendation by i) explicitly extracting shared item attributes to guide the learning of shared user preferences and ii) disentangling these preferences to identify specific user interests transferred between domains. CoPD introduces coherence constraints on item embeddings of shared and specific domains, aiding in extracting shared attributes. Moreover, it utilizes these attributes to guide the disentanglement of user preferences into separate embeddings for interest and conformity through a popularity-weighted loss. Experiments conducted on real-world datasets demonstrate the superior performance of our proposed CoPD over existing competitive baselines, highlighting its effectiveness in enhancing cross-domain recommendation performance.