App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 7

IKFCL: intention-aware knowledge fusion model for contrastive recommendation

Zhao Chenyanga
He Ping'anb
Dai Qic
a. School of Computer Science & Technology (School of Artificial Intelligence), b. School of Science, c. School of Life Science & Medicine, Zhejiang Sci-Tech University, Hangzhou 310018, China
DOI: 10.19734/j.issn.1001-3695.2025.11.0478

Abstract

Existing recommendation methods suffer from limited performance due to data sparsity, semantic gaps, and the difficulty of accurately modeling user intents. This paper proposes an intention-aware knowledge fusion contrastive learning recommendation model, termed IKFCL. The model optimizes recommendation performance from three aspects: user intent modeling, dual-layer noise filtering, and adaptive multi-source information fusion. First, an intention-aware module captures latent motivations behind user interactions and dynamically models multi-level user preferences. Second, an intention-driven dual-layer noise filtering mechanism removes low-confidence edges from both the knowledge graph and interaction graph, improving semantic propagation purity and representation quality. Finally, a gating-based cross-view contrastive learning strategy adaptively balances the contributions of knowledge and interaction views, enhancing the discriminability and robustness of learned representations. Experiments conducted on three public datasets—Last-FM, MovieLens-1M, and Alibaba-iFashion—demonstrate that IKFCL achieves average improvements of 2.3% in Recall and 2.7% in NDCG over mainstream baseline methods, verifying the effectiveness of the proposed model in intent modeling, noise filtering, and knowledge-enhanced recommendation tasks.

Foundation Support

国家自然科学基金资助项目(62572442)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.11.0478
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 7

Publish History

[2026-03-27] Accepted Paper

Cite This Article

赵晨炀, 贺平安, 代琦. IKFCL：基于意图感知与知识融合的增强对比学习推荐模型 [J]. 计算机应用研究, 2026, 43 (7). (2026-03-27). https://doi.org/10.19734/j.issn.1001-3695.2025.11.0478. (Zhao Chenyang, He Ping'an, Dai Qi. IKFCL: intention-aware knowledge fusion model for contrastive recommendation [J]. Application Research of Computers, 2026, 43 (7). (2026-03-27). https://doi.org/10.19734/j.issn.1001-3695.2025.11.0478. )

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  • Application Research of Computers Monthly Journal
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    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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