.png){kind=icon}
返回
MRP2Rec: Exploring Multiple-Step Relation Path Semantics for Knowledge Graph-Based Recommendations
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2020.3011279.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Knowledge graphs (KGs) have been proven to be effective for improving the performance of recommender systems. KGs can store rich side information and relieve the data sparsity problem. There are many linked attributes between entity pairs (e.g., items and users) in KGs, which can be called multiplestep relation paths. Existing methods do not sufficiently exploit the information encoded in KGs. In this paper, we propose MRP2Rec to explore various semantic relations in multiple-step relation paths to improve recommendation performance. The knowledge representation learning approach is used in our method to learn and represent multiple-step relation paths, and they are further utilized to generate prediction lists by inner products in top-K recommendations. Experiments on two real-world datasets demonstrate that our model achieves higher performance compared with many state-of-the-art baselines.
Keyword:
Recommender systems
knowledge graph
semantic representation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
A learning path recommendation model based on a multidimensional knowledge graph framework for e-learning基于多维知识图谱框架的e-learning学习路径推荐模型
PREAI
分享收藏InSight coordinates determination from direct-to-Earth radio-tracking and Mars topography model
PREAI
分享收藏Dynamic patterning by the Drosophila pair-rule network reconciles long-germ and short-germ segmentation.png)
PLOS Biology
OAAI
PLOS BiologyIF0
OAAI分享收藏分享收藏HI2Rec: Exploring Knowledge in Heterogeneous Information for Movie RecommendationHI2Rec: 探索异构信息中的知识以进行电影推荐IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏分享收藏
查看更多内容