{"ID":2871247,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.11094","arxiv_id":"2509.11094","title":"SPARK: Adaptive Low-Rank Knowledge Graph Modeling in Hybrid Geometric Spaces for Recommendation","abstract":"Knowledge Graphs (KGs) enhance recommender systems but face challenges from inherent noise, sparsity, and Euclidean geometry's inadequacy for complex relational structures, critically impairing representation learning, especially for long-tail entities. Existing methods also often lack adaptive multi-source signal fusion tailored to item popularity. This paper introduces SPARK, a novel multi-stage framework systematically tackling these issues. SPARK first employs Tucker low-rank decomposition to denoise KGs and generate robust entity representations. Subsequently, an SVD-initialized hybrid geometric GNN concurrently learns representations in Euclidean and Hyperbolic spaces; the latter is strategically leveraged for its aptitude in modeling hierarchical structures, effectively capturing semantic features of sparse, long-tail items. A core contribution is an item popularity-aware adaptive fusion strategy that dynamically weights signals from collaborative filtering, refined KG embeddings, and diverse geometric spaces for precise modeling of both mainstream and long-tail items. Finally, contrastive learning aligns these multi-source representations. Extensive experiments demonstrate SPARK's significant superiority over state-of-the-art methods, particularly in improving long-tail item recommendation, offering a robust, principled approach to knowledge-enhanced recommendation. Implementation code is available at https://github.com/Applied-Machine-Learning-Lab/SPARK.","short_abstract":"Knowledge Graphs (KGs) enhance recommender systems but face challenges from inherent noise, sparsity, and Euclidean geometry's inadequacy for complex relational structures, critically impairing representation learning, especially for long-tail entities. Existing methods also often lack adaptive multi-source signal fusi...","url_abs":"https://arxiv.org/abs/2509.11094","url_pdf":"https://arxiv.org/pdf/2509.11094v1","authors":"[\"Binhao Wang\",\"Yutian Xiao\",\"Maolin Wang\",\"Zhiqi Li\",\"Tianshuo Wei\",\"Ruocheng Guo\",\"Xiangyu Zhao\"]","published":"2025-09-14T05:12:46Z","proceeding":"cs.IR","tasks":"[\"cs.IR\"]","methods":"[\"Graph Neural Network\"]","has_code":false,"code_links":[{"ID":609839,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2871247,"paper_url":"https://arxiv.org/abs/2509.11094","paper_title":"SPARK: Adaptive Low-Rank Knowledge Graph Modeling in Hybrid Geometric Spaces for Recommendation","repo_url":"https://github.com/Applied-Machine-Learning-Lab/SPARK","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}