User group-enhanced user feature distribution transfer framework for non-overlapping cross-domain recommendations

Abstract

Cross-domain recommendation (CDR) aims to alleviate data sparsity in the target domain by leveraging rich information from source domains. Most existing approaches rely on overlapping information to transfer knowledge, but in real scenarios, these correspondences are often unknown. This makes it critical to develop CDR systems without overlapping information. However, such CDR systems still face user feature bias between domains and ignore the importance of sparse interaction information from the target domain, resulting in sub-optimal recommendations performance. To address challenges, we propose a User Group-enhanced User Feature Distribution Transfer framework (UGUFDT) for CDR. Specifically, it first utilizes a User Feature Separation Network bridges domains by constructing a cross-domain user–cluster graph to capture transferable user features, while User Feature Reconstructor refines unbiased user representations through reconstruction factors to build an inverse user–cluster graph, filter out source domain-specific noises. Then, we introduce three types of loss function – Difference Loss, Similarity Loss, Reconstruction Loss – to reduce feature distribution discrepancies between domains. Furthermore, to fully exploit interactions in target domain, we propose a User–Group Graph with a Soft Allocation Mechanism, which aggregates group-level preferences to enhance user representations. Finally, a Prediction Layer with a Fusion Mechanism integrates both cross-domain transferable knowledge and target-domain preferences to generate more accurate recommendations. Experiments on three publicly available datasets – ML, AB, and AM – demonstrate that the proposed model significantly outperforms state-of-the-art models on the HR and NDCG evaluation metrics, validating the effectiveness of our model.