融合知识图谱的推荐系统研究进展

doi:10.3785/j.issn.1008-973X.2023.08.006

浙江大学学报(工学版)

2023, Vol. 57

Issue (8): 1527-1540 DOI: 10.3785/j.issn.1008-973X.2023.08.006

计算机技术

融合知识图谱的推荐系统研究进展

王慧欣(

),童向荣*(

)

烟台大学计算机与控制工程学院，山东烟台 264005

Research progress of recommendation system based on knowledge graph

Hui-xin WANG(

),Xiang-rong TONG*(

)

School of Computer and Control Engineering, Yantai University, Yantai 264005, China

全文: PDF(1419 KB) HTML

摘要：

针对推荐系统存在的数据稀疏、冷启动、推荐可解释性低、个性化不足等问题，分析知识图谱在推荐系统中的融入情况.从推荐系统的需求、知识图谱的相关概念、推荐系统与知识图谱的融合方式3个方面，对当前推荐系统存在的问题及推荐系统融合知识图谱后的解决方案进行概括. 总结近年来通过结合注意力机制、神经网络、强化学习方法，采用取舍节点、整合节点、探索路径等原理充分利用知识图谱中复杂结构信息，从而提升推荐系统满意度. 提出融合知识图谱的推荐系统所面临的知识图谱完备性、动态性、高阶关系可利用度以及推荐性能方面的挑战及未来可能的发展方向.

关键词： 知识图谱; 推荐系统; 协同过滤; 注意力机制; 图嵌入

Abstract:

Aiming at the problems of data sparsity, cold start, low interpretability of recommendation, and insufficient personalization in recommender system, the integration of knowledge graph into recommender system was analyzed. From the demand of recommender system, the concept of knowledge graph, and the integration approach of recommender system and knowledge graph, the problems of current recommender system and the solutions of recommender system after integrating knowledge graph were summarized. It was reviewed that, in recent years, the attention mechanism, neural network and reinforcement learning methods were combined, by which the principles of node trade-off, node integration, and paths exploring were used to make full use of the complex structural information in knowledge graph, so as to improve the satisfaction degree with the recommender system. The challenges and possible future development direction of the recommender system integrating the knowledge graph were put forward in terms of knowledge graph completeness, dynamics, availability of higher-order relationships, and the performance of the recommendation.

Key words: knowledge graph recommendation system collaborative filtering attention mechanism graph embedding

收稿日期: 2022-07-29 出版日期: 2023-08-31

CLC:

TP 391

基金资助: 国家自然科学基金资助项目(62072392, 61972360)；山东省重大科技创新工程资助项目(2019522Y020131)；烟台市重点实验室资助项目

通讯作者: 童向荣 E-mail: huixin_king@163.com;xr_tong@163.com

作者简介: 王慧欣（1996—），女，硕士，从事知识图谱、推荐系统研究. orcid.org/0000-0001-7515-1063. E-mail： huixin_king@163.com

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引用本文:

王慧欣,童向荣. 融合知识图谱的推荐系统研究进展[J]. 浙江大学学报(工学版), 2023, 57(8): 1527-1540.

Hui-xin WANG,Xiang-rong TONG. Research progress of recommendation system based on knowledge graph. Journal of ZheJiang University (Engineering Science), 2023, 57(8): 1527-1540.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2023.08.006 或 https://www.zjujournals.com/eng/CN/Y2023/V57/I8/1527

图 1 基于内容的推荐算法

图 2 基于用户和物品的CF推荐图

图 3 推荐系统常用辅助信息

图 4 知识图谱示例图

图 5 基于知识图谱的推荐系统示意图

表 1 使用不同KG方式的推荐算法对比列表

图 6 Trans系列翻译模型

图 7 协同知识图谱

图 8 注意力网络示意图

图 9 KG中目标用户实体节点的两层感受域

图 10 强化学习中Agent与环境的交互图

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