动态筛选知识图谱信息，提升推荐系统准确率
Dynamic Knowledge Selector and Evaluator for recommendation with Knowledge Graph
- 基于协同信号动态评估邻居节点贡献，筛选有用知识
- 在三个公开数据集上超越现有最优模型，提升推荐效果
- 适合研究知识图谱与推荐融合的学者参考
近年来，推荐系统普遍利用知识图谱提供的边信息，并结合图网络的高阶连通性优势。然而，该方法受限于标签稀疏、难以有效学习图结构，且知识图谱中大量噪声实体会影响推荐精度。为缓解上述问题，本文提出一种由协同信号引导的动态知识选择与评估方法，用于提炼知识图谱中的有效信息。具体地，采用链路路径评估器（Chain Route Evaluator）衡量不同邻域对推荐任务的贡献，并通过知识选择策略在评估前过滤低信息量知识。我们在三个公开数据集上进行基线模型对比与消融实验，结果表明所提模型优于当前最先进方法，且各模块有效性经消融实验验证。
原文摘要 · Abstract (English)
In recent years recommendation systems typically employ the edge information provided by knowledge graphs combined with the advantages of high-order connectivity of graph networks in the recommendation field. However, this method is limited by the sparsity of labels, cannot learn the graph structure well, and a large number of noisy entities in the knowledge graph will affect the accuracy of the recommendation results. In order to alleviate the above problems, we propose a dynamic knowledge-selecting and evaluating method guided by collaborative signals to distill information in the knowledge graph. Specifically, we use a Chain Route Evaluator to evaluate the contributions of different neighborhoods for the recommendation task and employ a Knowledge Selector strategy to filter the less informative knowledge before evaluating. We conduct baseline model comparison and experimental ablation evaluations on three public datasets. The experiments demonstrate that our proposed model outperforms current state-of-the-art baseline models, and each modules effectiveness in our model is demonstrated through ablation experiments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。