融合知识图谱与图片特征的推荐模型

陈乔松; 郭傲东; 杜雨露; 张怡文; 朱越

doi:10.11999/JEIT210230

融合知识图谱与图片特征的推荐模型

doi: 10.11999/JEIT210230

1.
重庆邮电大学计算机科学与技术学院重庆 400065
2.
安徽新华学院信息工程学院合肥 230000
3.
河南农业大学信息与管理科学学院郑州 450046

基金项目: 重庆邮电大学社科项目(K2021-114)

详细信息

作者简介:
陈乔松：男，1978年生，副教授，研究方向为图像检索系统等

郭傲东：男，1997年生，硕士生，研究方向为个性化推荐算法研究

杜雨露：男，1987年生，讲师，研究方向为个性化推荐算法研究等

张怡文：女，1980年生，教授，研究方向为个性化推荐算法研究

朱越：女，1997年生，硕士生，研究方向为知识工程

通讯作者:
杜雨露　duyl@cqupt.edu.cn

中图分类号: TN911.73; TP311
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- 被引次数: 0
出版历程
- 收稿日期: 2021-03-22
- 修回日期: 2022-01-03
- 录用日期: 2022-01-05
- 网络出版日期: 2022-01-27
- 刊出日期: 2022-05-25

Recommendation Model by Integrating Knowledge Graph and Image Features

1.
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
College of Information Engineering, Anhui Xinhua University, Hefei 230000, China
3.
School of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China

Funds: The Social Science Project of Chongqing University of Posts and Telecommunications (K2021-114)

摘要

摘要: 目前知识图谱研究主要面向信息检索、自然语言理解等领域，在推荐系统中融合知识图谱成为推荐领域学者广泛关注的问题。为了解决单一知识图谱忽略的丰富知识信息，该文对知识图谱进行多模态扩展，并提出一种融合知识图谱与图片特征的推荐模型(KG-I)。不同于其他基于知识图谱的推荐算法，该方法增加视觉嵌入、知识嵌入和结构嵌入去挖掘用户项目之间的隐式反馈信息。该模型利用深度游走模型(Deep Walk)捕获空间结构的方法和波纹网络模型(RippleNet)挖掘知识图谱的知识表达的思想，并且考虑图片对用户偏好的影响，有效地将信息进行融合，并在真实数据集上与其他模型实验比较，研究多种特征的影响，分析不同稀疏度数据下的表现。结果表明，融合知识图谱与图片特征的个性化推荐模型完全优于其他的对比算法并且有效缓解数据稀疏情况。
- 图像处理 /
- 知识图谱 /
- 数据融合 /
- 多模态
Abstract: At present, the study of knowledge graph focuses mainly on information retrieval, natural language understanding and other fields. Integrating knowledge graph with recommendation system has been concerned by scholars in the field. In order to mine the rich information ignored in knowledge graph, the knowledge graph is extended to multimodal and a recommendation model that incorporates Knowledge Graph with Image (KG-I) features is proposed. Different from other recommendation algorithms, visual embedding, knowledge embedding and structure embedding are combined to capture implicit feedback between user-items. The Deep Walk is used to capture the spatial structure and the ideal of RippleNet to retain the semantic features of knowledge graph, and the effect of images on preference is considered to integrate information. Compared with other models on the real data set, the influence of various features is studied, and the performance of different sparsity data is analyzed. The results show that the personalized recommendation model based on knowledge graph and image features outperforms other algorithms and the data sparsity can be alleviated.
- Image processing /
- Knowledge graph /
- Data fusion /
- Multimodality

HTML全文

图 1 KG-I框架图

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图 2 VGG16框架图

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图 3 模型参数对AUC的影响

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图 4 模型权重对AUC的影响

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图 5 AUC和Acc评价指标随训练轮数的变化图

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图 6 不同模型的召回率和准确率

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图 7 不同特征的召回率和准确率

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图 8 不同数据稀疏度的召回率

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表 1 实验数据

数据集	指标类型	指标值	数据集	指标类型	指标值
电影评分数据	用户数量	610	知识图谱数据	用户实体数量	610
	项目数量	9742		项目实体数量	9742
	评分数量	100836		演职人员关系	52904
	评分级别	0.5, 1, ···, 5		电影类别关系	24226
	电影海报数量	9730		项目图片数量	9730

下载: 导出CSV

表 2 在MovieLens集上各种算法AUC和Acc对比

	BPRMF	DKN	RippleNet	KG-I	KG-SS	KG-S
AUC	0.7137	0.7925	0.8353	0.8544	0.8438	0.8345
Acc	0.6396	0.7268	0.7769	0.7880	0.7756	0.7802

下载: 导出CSV

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