[1]马甜甜,杨长春,严鑫杰,等.融合知识图谱和轻量级图卷积网络推荐系统的研究[J].智能系统学报,2022,17(4):721-727.[doi:10.11992/tis.202107016]
MA Tiantian,YANG Changchun,YAN Xinjie,et al.Research on the fusion of knowledge graph and lightweight graph convolutional network recommendation system[J].CAAI Transactions on Intelligent Systems,2022,17(4):721-727.[doi:10.11992/tis.202107016]
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MA Tiantian,YANG Changchun,YAN Xinjie,et al.Research on the fusion of knowledge graph and lightweight graph convolutional network recommendation system[J].CAAI Transactions on Intelligent Systems,2022,17(4):721-727.[doi:10.11992/tis.202107016]
融合知识图谱和轻量级图卷积网络推荐系统的研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第4期
页码:
721-727
栏目:
学术论文—自然语言处理与理解
出版日期:
2022-07-05
- Title:
- Research on the fusion of knowledge graph and lightweight graph convolutional network recommendation system
- Keywords:
- graph convolutional network; knowledge graph; recommendation system; embedded propagation; collaborative filtering; sparsity; neighborhood information; lightweight aggregator
- 分类号:
- TP391
- 摘要:
- 基于协同过滤的算法是推荐系统中最重要的方法，由于冷启动和数据稀疏性的特点，限制了其推荐性能。为了应对以上问题，提出了知识图谱和轻量级图卷积网络推荐系统相结合的模型，该模型通过将知识图谱中的各个实体（项目）进行多次迭代嵌入传播以获取更多的高阶邻域信息，通过轻量聚合器进行聚合，进而预测用户和项目之间的评分。最后，在3个真实的数据集上MovieLens-20M、Last.FM和Book-Crossing的实验结果表明，该模型与其他基准模型相比可以得到较好的性能。
- Abstract:
- The algorithm based on collaborative filtering is the most important method in the recommendation system. However, the cold start and data sparsity characteristics limit its recommendation performance. We propose a model that combines a knowledge graph and a lightweight graph convolutional network recommendation system to address the aforementioned issues. The model embeds and propagates multiple items in the knowledge graph to obtain more high-order neighborhood information. It aggregates through a lightweight aggregator to predict the score between users and items. Finally, the experimental findings of MovieLens-20M, Last.FM and Book-Crossing on three real datasets show that compared with other benchmark models, this model can achieve better performance.
- 参考文献/References:
-
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备注/Memo
收稿日期:2021-07-08。
基金项目:国家自然科学基金项目（51877013）; 江苏省研究生科研创新基金项目（KYCX21_2842）.
作者简介:马甜甜，硕士研究生，主要研究方向为数据挖掘、推荐系统;杨长春，教授，主要研究方向为数据库系统与数据挖掘。发表学术论文50余篇;严鑫杰，硕士研究生，主要研究方向为数据挖掘、命名实体识别。
通讯作者:杨长春. E-mail：ycc@cczu.edu.cn
更新日期/Last Update:
1900-01-01