计算机科学 ›› 2023, Vol. 50 ›› Issue (3): 129-138.doi: 10.11896/jsjkx.220300004
周明强, 代开浪, 吴全旺, 朱庆生
ZHOU Mingqiang, DAI Kailang, WU Quanwang, ZHU Qingsheng
摘要： 异构信息网络(Heterogeneous Information Network,HIN)包含了丰富的语义信息,利用其进行评分预测已成为缓解推荐系统数据稀疏性问题的一个重要途径。然而,传统采用元路径来提取HIN语义信息的方法忽略了元路径中的评分信息,从而导致元路径无法精确捕获用户和推荐项目之间的语义相似性,同时也未能良好区分不同元路径的重要性。为了解决这两个问题,首先提出了一种带有评分限制的元路径以获取更准确的HIN语义信息,利用这些信息构建用户和项目多层网络;然后结合图卷积网络和注意力机制设计了一个用于评分预测的神经网络,通过多通道图卷积有效地表示了HIN的多种语义信息,采用注意力机制区分不同元路径的重要性,弥补了传统方法的不足;最后融合了用户和项目的属性信息,进一步提高了评分预测的准确性。在Douban Book和Yelp数据集上的实验结果表明所提模型明显优于对比的基线模型,尤其在数据稀疏的情况下,均方根误差比基线模型最多减少了50%,从而验证了所提模型的优越性。
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