Multi-factor disentangled graph neural networks for session-based new item recommendation

Xinning Li; Qian Gao; Jun Fan; Lujie Feng; Xinning Li; Qian Gao; Jun Fan; Lujie Feng

doi:10.3934/math.20251024

AIMS Mathematics

2025, Volume 10, Issue 10: 23067-23083. doi: 10.3934/math.20251024

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Multi-factor disentangled graph neural networks for session-based new item recommendation

1.
Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, Shandong, China
2.
Shandong Engineering Research Center of Big Data Applied Technology, Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, Shandong, China
3.
Shandong Provincial Key Laboratory of Industrial Network and Information System Security, Shandong Fundamental Research Center for Computer Science, Jinan 250014, Shandong, China
4.
China Telecom Digital Intelligence Technology Co, Ltd, No.1999, Shunhua road, Jinan 250101, Shandong, China

Received: 17 June 2025 Revised: 22 September 2025 Accepted: 23 September 2025 Published: 11 October 2025
MSC : 68T07, 68T20

Recent studies have shown that graph neural networks for session-based recommendation systems typically recommend old items, making it difficult to recommend new items to users, leading to the phenomenon of the "information cocoon". To address this issue, this paper introduces a Multi-Factor Disentangled Graph Neural Network for Session-Based New Item Recommendation (MFD-GNN), which considers both the embedding of new items and user intent from a multi-factor perspective. First, item embeddings from sessions are generated across multiple factors using a disentangled network. By leveraging item classification and attribute information, new item embeddings are inferred through zero-shot learning. Attention weights are assigned to each factor to capture user intent across different factors, enabling reasonable recommendations for new items. Experiments are conducted on two publicly available datasets, and the results are compared with those of leading recommendation models. The findings demonstrate that the proposed method surpasses current models in performance. These experimental outcomes confirm the approach's effectiveness and its advantages over existing methods.
- session-based recommendation,
- graph neural network,
- disentangled representation learning,
- new item recommendation
Citation: Xinning Li, Qian Gao, Jun Fan, Lujie Feng. Multi-factor disentangled graph neural networks for session-based new item recommendation[J]. AIMS Mathematics, 2025, 10(10): 23067-23083. doi: 10.3934/math.20251024

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Abstract

Recent studies have shown that graph neural networks for session-based recommendation systems typically recommend old items, making it difficult to recommend new items to users, leading to the phenomenon of the "information cocoon". To address this issue, this paper introduces a Multi-Factor Disentangled Graph Neural Network for Session-Based New Item Recommendation (MFD-GNN), which considers both the embedding of new items and user intent from a multi-factor perspective. First, item embeddings from sessions are generated across multiple factors using a disentangled network. By leveraging item classification and attribute information, new item embeddings are inferred through zero-shot learning. Attention weights are assigned to each factor to capture user intent across different factors, enabling reasonable recommendations for new items. Experiments are conducted on two publicly available datasets, and the results are compared with those of leading recommendation models. The findings demonstrate that the proposed method surpasses current models in performance. These experimental outcomes confirm the approach's effectiveness and its advantages over existing methods.

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