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融合知识图谱与协同过滤的微地图推荐

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  • NETL
  • NSTL
  • 万方数据
针对推荐系统中数据稀疏问题,传统的协同过滤算法无法捕捉辅助信息之间的相关性,从而降低了推荐的准确性.为此,本文提出融合知识图谱的协同过滤模型(Knowledge Graph Embedding Collaborative Filtering,KGCF),引入知识图谱作为辅助信息,利用知识图谱中多源结构性的数据来缓解数据稀疏问题.KGCF模型结合知识图谱的语义信息和协同过滤的偏好信息,能够挖掘出用户和微地图的隐语义交互信息,从而达到"千人千面"的推荐效果.①融合知识图谱和协同过滤算法对微地图数据集进行采集训练;②通过皮尔逊相关系数计算出用户之间的相似矩阵,并对稀疏的评分矩阵进行隐语义矩阵分解,采用基准(Baseline)得到用户和微地图地名的偏好信息;③通过知识图谱将微地图语义信息转化为低维向量,采用余弦相似度计算出微地图地名之间的相似矩阵;④将用户和微地图地名结合为一个推荐结果集.通过在微地图数据集上实验,证明了本文提出的KGCF模型能有效解决数据稀疏,可准确为用户推荐感兴趣的微地图.
WeMap Recommendation by Fusion of Knowledge Graph and Collaborative Filtering
Based on sparse matrix,traditional collaborative filtering techniques usually have a low recommendation accuracy,since they cannot capture the correlations between auxiliary information from the sparse data.To fill the gap,this paper proposes a Knowledge Graph embedding Collaborative Filtering(KGCF)model to improve recommendation accuracy.In this model,the knowledge graph is introduced as auxiliary information,taking advantage of its multi-source structured data to alleviate the problem of data sparsity.By combining the semantic information of the knowledge graph and the preference information of collaborative filtering,the KGCF model can mine the interaction between users and WeMap to implement customized recommendations.Specifically,the knowledge graph and collaborative filtering algorithm are first combined to train the model on WeMap datasets.Secondly,the similarity matrix between users is calculated using the Pearson correlation coefficient,and the cryptic meaning matrix is decomposed through a sparse scoring matrix.In addition,the preference information of users and place names of WeMap is obtained using Baseline.Then,the semantic information of each object is transformed into a low dimension vector by the knowledge graph,and the similarity matrix between WeMap place names is calculated by cosine similarity.Finally,the users and place names of the WeMap are integrated into a recommendation result set.The experiments on WeMap datasets prove that the proposed KGCF model can effectively solve data sparsity and accurately recommend WeMaps of interest for users.

knowledge graphcollaborative filteringsparse dataWeMaprecommendation systemstructured dataimplicit semantics

牛雪磊、杨军、闫浩文

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兰州交通大学测绘与地理信息学院,兰州 730070

地理国情监测技术应用国家地方联合工程研究中心,兰州 730070

甘肃省地理国情监测工程实验室,兰州 730070

知识图谱 协同过滤 数据稀疏 微地图 推荐系统 结构性数据 隐语义

国家自然科学基金项目国家自然科学基金项目2021年度中央引导地方科技发展资金兰州市人才创新创业项目兰州交通大学天佑创新团队

42261067618620392021-512020-RC-22TY202002

2024

10.12082/dqxxkx.2024.220581

地球信息科学学报
中国科学院地理科学与资源研究所

地球信息科学学报

CSTPCD北大核心
影响因子：1.004
ISSN：1560-8999
年,卷(期)：2024.26(4)
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