计算机科学 ›› 2025, Vol. 52 ›› Issue (7): 103-109.doi: 10.11896/jsjkx.240600120
罗旭阳, 谭智一
LUO Xuyang, TAN Zhiyi
摘要： 基于知识图谱的推荐模型通过捕捉交互物品在知识图谱上的实体关联,实现对用户偏好的精准建模,从而提高推荐的准确性。然而,现有工作忽略了交互图的噪声问题和稀疏性问题,限制了模型对实体关联的捕捉效率,导致用户偏好建模出现偏差,从而无法获得最优结果。为了解决上述问题,提出了一种基于知识感知的图优化推荐模型(Knowledge-aware Graph Refinement Network,KGRN)。具体来说,首先设计了一个图修剪模块,利用知识图谱的语义信息来动态修剪交互图中的噪声交互;然后设计了一个图构建模块来缓解交互图的数据稀疏性,提高模型挖掘用户偏好的实体能力,增强用户偏好建模。为了验证KGRN的有效性,在3个基准数据集上进行了对比实验。相较于现有模型,KGRN在MovieLens-1M上的表现提升了2.97%,在Amazon-Book上的表现提升了1.69%,在BookCrossing上的表现提升了2.22%。实验结果证明了所提模型的有效性。
中图分类号:
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