Enhanced Recommendation System Based on Attenuation Propagation for Knowledge Graphs

doi:10.3778/j.issn.1002-8331.2201-0303

Abstract

Abstract: At present, the recommendation method based on knowledge graphs does not make enough use of the information from paths of user interest propagation on knowledge graphs, which could not take into account the path propagation among the different levels. To address these problems, an enhanced recommendation system based on attenuation propagation for knowledge graphs（RSAP） is proposed. RSAP advances an inter-layer and an intra-layer interest propagation method, in terms of user interest graphs on knowledge graphs, to extract user interest embedding along different directions by using attenuation factors which reflect the change of user interest. And RSAP uses a purification network with residual blocks that can capture the focus of interest embedding to gain final user embedding for prediction. Experimental results on real-world datasets show that the RSAP outperforms state-of-the-art methods.

Key words: recommendation system, knowledge graph, attenuation propagation

摘要： 基于知识图谱的推荐方法对于用户兴趣在知识图谱上的传播路径信息利用不足，没有充分考虑不同层次之间的路径传播。针对以上问题，提出基于衰减传播的知识图谱增强推荐系统（enhanced recommendation system based on attenuation propagation for knowledge graphs，RSAP）。该模型设计基于知识图谱的兴趣图层内传播方法和层间传播方法，利用反映用户兴趣强弱变化的衰减因子，分别沿着知识图谱的不同方向提取用户兴趣表示，使用基于残差块的提纯网络来捕获用户兴趣的焦点，从而得到用于计算推荐得分的最终用户表示，并生成推荐结果。在三个公共知识图谱数据集上的对比实验结果证明，RSAP在多个基准评价指标上取得较好的效果，优于其他对比算法。

关键词: 推荐系统, 知识图谱, 衰减传播

CAO Yukun, FANG Yixin, MIAO Zeyu, LI Yunfeng. Enhanced Recommendation System Based on Attenuation Propagation for Knowledge Graphs[J]. Computer Engineering and Applications, 2023, 59(10): 180-186.

曹渝昆, 方一新, 苗泽宇, 李云峰. 基于衰减传播的知识图谱增强推荐系统[J]. 计算机工程与应用, 2023, 59(10): 180-186.

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