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Special Topics in Data Analysis and Knowledge Discovery
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 2
361-367,374

node2vec-side fusion knowledge representation for personalized recommendation

Ni Wenkai
Du Yanhui
Ma Xingbang
Lyu Haibin
College of Information & Cyber Security, People's Public Security University of China, Beijing 100038, China
DOI: 10.19734/j.issn.1001-3695.2023.06.0257

Abstract

The knowledge graph in the recommendation system plays a vital role in the recommendation effect of the system, and the knowledge representation in the graph becomes a key factor affecting the recommendation system, which has become one of the current research hotspots. This paper proposed a node2vec-based knowledge representation node2vec-side based on the traditional node2vec model by adding relational representation and diversifing wandering strategy to the structural characteristics of the knowledge graph in recommendation system, which combined with the knowledge graph network structure of recommendation system to explore the potential association relationship between nodes of large-scale recommendation entities, reduced the complexity of the representation and improved interpretability. After time complexity analysis, it could be seen that the proposed knowledge representation is lower than Trans series and RGCN in terms of complexity. Link prediction experiments were conducted on the traditional knowledge graph datasets FB15K, WN18, and recommendation domain datasets MovieLens-1M, Book-Crossing, Last. FM respectively. The experimental results show that on the MovieLens-1M dataset, hits@10 improves 5.5%~12.1% and MRR improves 0.09~0.24, respectively. On the Book-Crossing dataset, hits@10 improves 3.5%~20.6%, and MRR improves 0.04~0.24 on average, respectively. And on the Last. FM dataset, hits@1 improves 0.3%~8.5% and MRR improves 0.04~0.16 on average. It is better than the existing algorithms and verifies the effectiveness of the proposed method.

Foundation Support

中国人民公安大学网络空间安全执法技术双一流专项资助项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.06.0257
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 2
Section: Special Topics in Data Analysis and Knowledge Discovery
Pages: 361-367,374
Serial Number: 1001-3695(2024)02-006-0361-07

Publish History

[2023-08-21] Accepted Paper
[2024-02-05] Printed Article

Cite This Article

倪文锴, 杜彦辉, 马兴帮, 等. 面向个性化推荐的node2vec-side融合知识表示 [J]. 计算机应用研究, 2024, 41 (2): 361-367,374. (Ni Wenkai, Du Yanhui, Ma Xingbang, et al. node2vec-side fusion knowledge representation for personalized recommendation [J]. Application Research of Computers, 2024, 41 (2): 361-367,374. )

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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