Improving Cross-domain Recommendation through Probabilistic Cluster-level Latent Factor Model--Extended Version

TL;DR

This paper introduces a Probabilistic Cluster-level Latent Factor model that enhances cross-domain recommendation by capturing domain diversities, outperforming existing methods on real-world datasets.

Contribution

The paper proposes a novel PCLF model that models domain diversities in cross-domain recommendation, extending beyond shared latent patterns.

Findings

PCLF outperforms state-of-the-art methods on multiple datasets.

Model effectively captures domain-specific differences.

Improves recommendation accuracy in sparse data scenarios.

Abstract

Cross-domain recommendation has been proposed to transfer user behavior pattern by pooling together the rating data from multiple domains to alleviate the sparsity problem appearing in single rating domains. However, previous models only assume that multiple domains share a latent common rating pattern based on the user-item co-clustering. To capture diversities among different domains, we propose a novel Probabilistic Cluster-level Latent Factor (PCLF) model to improve the cross-domain recommendation performance. Experiments on several real world datasets demonstrate that our proposed model outperforms the state-of-the-art methods for the cross-domain recommendation task.