A Unified Framework for Cross-Domain and Cross-System Recommendations

TL;DR

This paper introduces a unified graph-based framework with attention and personalized training strategies to improve recommendation accuracy across multiple datasets and scenarios, including dual-target, multi-target, and combined cross-domain and cross-system recommendations.

Contribution

It proposes a novel unified framework called GA that handles multiple recommendation scenarios simultaneously, incorporating heterogeneous graphs, attention mechanisms, and personalized training to prevent negative transfer.

Findings

GA models outperform state-of-the-art methods on four real-world datasets.

The personalized training strategy effectively reduces negative transfer.

The framework successfully improves recommendation accuracy across all tested scenarios.

Abstract

Cross-Domain Recommendation (CDR) and Cross-System Recommendation (CSR) have been proposed to improve the recommendation accuracy in a target dataset (domain/system) with the help of a source one with relatively richer information. However, most existing CDR and CSR approaches are single-target, namely, there is a single target dataset, which can only help the target dataset and thus cannot benefit the source dataset. In this paper, we focus on three new scenarios, i.e., Dual-Target CDR (DTCDR), Multi-Target CDR (MTCDR), and CDR+CSR, and aim to improve the recommendation accuracy in all datasets simultaneously for all scenarios. To do this, we propose a unified framework, called GA (based on Graph embedding and Attention techniques), for all three scenarios. In GA, we first construct separate heterogeneous graphs to generate more representative user and item embeddings. Then, we propose an element-wise attention mechanism to effectively combine the embeddings of common entities (users/items) learned from different datasets. Moreover, to avoid negative transfer, we further propose a Personalized training strategy to minimize the embedding difference of common entities between a richer dataset and a sparser dataset, deriving three new models, i.e., GA-DTCDR-P, GA-MTCDR-P, and GA-CDR+CSR-P, for the three scenarios respectively. Extensive experiments conducted on four real-world datasets demonstrate that our proposed GA models significantly outperform the state-of-the-art approaches.