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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 12
3590-3596

Collaborative filtering recommendation algorithm based on knowledge graph embedding

Zhang Yihan1a,1b
Wang Wei1a,1b,2
Liu Huazhen1a,1b
Gu Renqian1a,1b
Hao Yaqi1a,1b
1. a. School of Information & Electrical Engineering, b. Hebei Key Laboratory of Security & Protection Information Sensing & Processing, Hebei University of Engineering, Handan Hebei 056038, China
2. School of Internet of Things Engineering, Jiangnan University, Wuxi Jiangsu 214122, China
DOI: 10.19734/j.issn.1001-3695.2021.05.0181

Abstract

One of the big challenges of using knowledge graphs for recommendations is how to capture the structured know-ledge of items and extract its semantic features. To solve this problem, this paper proposed a collaborative filtering recommendation algorithm based on knowledge graph embedding(KGECF). Firstly, it extracted the knowledge information related to items from the Freebase knowledge graph and linked with historical interactive items to construct knowledge subgraphs. Then it obtained the representation of entities and relations in the sub-graph using the Xavier-TransR method based on TransR. This paper designed an end-to-end joint learning model to embed structured information and historical preference information into a unified vector space. Finally, it used the collaborative filtering method to further calculate these vectors to generate an accurate re-commendation list. Experimental results on two public datasets MovieLens-1M and Amazon-book show that the proposed algorithm is superior to the baseline algorithms in terms of precision, recall, F1 value and NDCG metrics. It means that the above method can integrate large-scale structured and unstructured data, while obtaining high precision recommendation results.

Foundation Support

国家自然科学基金资助项目(61802107)
教育部—中国移动科研基金资助项目(MCM20170204)
河北省高等学校科学技术研究资助项目(ZD2020171)
江苏省博士后科研资助计划项目(1601085C)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.05.0181
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 12
Section: Algorithm Research & Explore
Pages: 3590-3596
Serial Number: 1001-3695(2021)12-012-3590-07

Publish History

[2021-12-05] Printed Article

Cite This Article

张屹晗, 王巍, 刘华真, 等. 基于知识图嵌入的协同过滤推荐算法 [J]. 计算机应用研究, 2021, 38 (12): 3590-3596. (Zhang Yihan, Wang Wei, Liu Huazhen, et al. Collaborative filtering recommendation algorithm based on knowledge graph embedding [J]. Application Research of Computers, 2021, 38 (12): 3590-3596. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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