Do All Nodes Benefit Equally from Knowledge Graphs? Adaptive Node-Aware KG Fusion for Recommendation

• URL: http://arxiv.org/abs/2609.05909v1
• Date: Sat, 05 Sep 2026 06:03:02 GMT
• Title: Do All Nodes Benefit Equally from Knowledge Graphs? Adaptive Node-Aware KG Fusion for Recommendation
• Abstract summary: We propose AdaKG, a novel KG-aware recommendation method that adaptively adjusts the contribution of auxiliary knowledge for each node.<n>AdaKG achieves strong performance compared with its baselines and the effectiveness of our adaptive fusion strategy.
• Score: 4.546840710582267
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: KG-aware recommendation has been widely studied to alleviate data sparsity by using knowledge graphs (KGs), which represent items, entities, and their relations as graphs and provide item-side knowledge. However, existing methods incorporate item knowledge without considering how much each user or item node should rely on it. As a result, they apply KG signals indiscriminately across nodes, even to nodes whose collaborative filtering (CF) signals from the interaction graph (IG) are already reliable. In this paper, we propose AdaKG (Adaptive Node-Aware KG Fusion), a novel KG-aware recommendation method that adaptively adjusts the contribution of auxiliary knowledge for each node. Since user-item interactions and item knowledge provide different types of signals, directly mixing them can distort the CF signals. To avoid this, AdaKG separately encodes the IG and KG with view-specific encoders, allowing each view to capture its own information. It then estimates how strongly each node should rely on item knowledge by measuring the stability of its CF signals under small adversarial perturbations, assigning a larger KG contribution to less stable nodes. Finally, AdaKG adaptively aligns the IG and KG embeddings in a shared space and fuses them according to the estimated node-wise reliance. Through experiments, we show that AdaKG achieves strong performance compared with its baselines and the effectiveness of our adaptive fusion strategy.

Related papers

• Heterogeneous Hypergraph Embedding for Recommendation Systems [45.49449132970778]
  We present a novel Knowledge-enhanced Heterogeneous Hypergraph Recommender System (KHGRec) KHGRec captures group-wise characteristics of both the interaction network and the KG, modeling complex connections in the KG. It fuses signals from the input graphs with cross-view self-supervised learning and attention mechanisms.
  arXiv  Detail & Related papers  (2024-07-04T06:09:11Z)
• On the Sweet Spot of Contrastive Views for Knowledge-enhanced Recommendation [49.18304766331156]
  We propose a new contrastive learning framework for KG-enhanced recommendation. We construct two separate contrastive views for KG and IG, and maximize their mutual information. Extensive experimental results on three real-world datasets demonstrate the effectiveness and efficiency of our method.
  arXiv  Detail & Related papers  (2023-09-23T14:05:55Z)
• Collective Knowledge Graph Completion with Mutual Knowledge Distillation [11.922522192224145]
  We study the problem of multi-KG completion, where we focus on maximizing the collective knowledge from different KGs. We propose a novel method called CKGC-CKD that uses relation-aware graph convolutional network encoder models on both individual KGs and a large fused KG. Experimental results on multilingual datasets have shown that our method outperforms all state-of-the-art models in the KGC task.
  arXiv  Detail & Related papers  (2023-05-25T09:49:40Z)
• Explainable Sparse Knowledge Graph Completion via High-order Graph Reasoning Network [111.67744771462873]
  This paper proposes a novel explainable model for sparse Knowledge Graphs (KGs) It combines high-order reasoning into a graph convolutional network, namely HoGRN. It can not only improve the generalization ability to mitigate the information insufficiency issue but also provide interpretability.
  arXiv  Detail & Related papers  (2022-07-14T10:16:56Z)
• Collaborative Knowledge Graph Fusion by Exploiting the Open Corpus [59.20235923987045]
  It is challenging to enrich a Knowledge Graph with newly harvested triples while maintaining the quality of the knowledge representation. This paper proposes a system to refine a KG using information harvested from an additional corpus.
  arXiv  Detail & Related papers  (2022-06-15T12:16:10Z)
• Knowledge Graph Contrastive Learning for Recommendation [32.918864602360884]
  We design a general Knowledge Graph Contrastive Learning framework to alleviate the information noise for knowledge graph-enhanced recommender systems. Specifically, we propose a knowledge graph augmentation schema to suppress KG noise in information aggregation. We exploit additional supervision signals from the KG augmentation process to guide a cross-view contrastive learning paradigm.
  arXiv  Detail & Related papers  (2022-05-02T15:24:53Z)
• DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN [59.160401038969795]
  We propose differentiable sampling on Knowledge Graph for Recommendation with GNN (DSKReG) We devise a differentiable sampling strategy, which enables the selection of relevant items to be jointly optimized with the model training procedure. The experimental results demonstrate that our model outperforms state-of-the-art KG-based recommender systems.
  arXiv  Detail & Related papers  (2021-08-26T16:19:59Z)
• Learning Intents behind Interactions with Knowledge Graph for Recommendation [93.08709357435991]
  Knowledge graph (KG) plays an increasingly important role in recommender systems. Existing GNN-based models fail to identify user-item relation at a fine-grained level of intents. We propose a new model, Knowledge Graph-based Intent Network (KGIN)
  arXiv  Detail & Related papers  (2021-02-14T03:21:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.