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IT4Innovations, VSB - Technical University of Ostrava

Academic institutioneurope · cz
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Research library20linked papers
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Selected work

Representative Papers

Combining Social Relations and Interaction Data in Recommender System With Graph Convolution Collaborative Filtering

Jun 03, 2025IEEE Access

To address the challenges of data sparsity, noise interference, and ineffective fusion of social influence with collaborative signals in social recommendation, this paper proposes a Robust Graph Convolutional Collaborative Filtering framework (R-GCCF). R-GCCF jointly models the user-item interaction graph and the social relation graph. It introduces, for the first time, an adaptive input denoising mechanism to suppress noise arising from sparse interactions, and enables dynamic, weighted integration of social influence and collaborative similarity within a unified GCN architecture. Additionally, it incorporates social regularization and confidence-weighted interaction modeling. Extensive experiments on multiple public benchmarks demonstrate that R-GCCF consistently outperforms state-of-the-art baselines—including NGCF, LightGCN, and SocialLGN—achieving absolute improvements of 12.7% in Recall@20 and 9.3% in NDCG@20, thereby validating its effectiveness and robustness.

2 citationsRead paper

Improvement Graph Convolution Collaborative Filtering with Weighted Addition Input

Mar 27, 2025Asian Conference on Intelligent Information and Database Systems

To address the problem that conventional graph neural network–based recommender systems neglect interaction weights between users and items—leading to insufficiently discriminative representations—this paper proposes a learnable weighted input mechanism. Specifically, it constructs an auxiliary weight graph to explicitly model heterogeneous influences of different users on the same item. This approach achieves, for the first time in GCN-based collaborative filtering, end-to-end learnable weighting of the adjacency matrix without relying on explicit social relations; instead, it infers implicit user associations solely from co-occurrence patterns in user-item interactions. Integrated into LightGCN and NGCF architectures, the proposed method consistently outperforms state-of-the-art baselines—including GCMC, NGCF, and LightGCN—across multiple public benchmarks, achieving up to a 12.6% improvement in Recall@20. The implementation is publicly available.

2 citationsRead paper
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