Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 6
1679-1685

Self-supervised graph learning of fusing multi-modal for video recommendation model

Yu Wentinga,b
Wu Yuna,b
Lin Jianb
a. State Key Laboratory of Public Big Data, b. College of Computer Science & Technology, Guizhou University, Guiyang 550025, China
DOI: 10.19734/j.issn.1001-3695.2022.11.0550

Abstract

Existing video recommendation methods introduce graph neural networks in the framework to model the user-video co-relation and learn the representation vectors of users and videos, but the redundant noise contained in the nodes may limit the modeling capability of the model. To this end, this paper proposed a new model that integrated multimodal self-supervised graph learning for video recommendation model(IMSGL-VRM). Firstly, this paper constructed a self-supervised graph neural network in the graph data augmentation mode to learn the node feature representation in the multimodal view to improve the generalization ability of the node representation. Secondly, in order to obtain the diversity of recommendation results, this paper designed a multi-interests extraction module which models users' multi-interests from their historical interactive video sequences. Finally, this paper integrated the multi-modal users' multi-interest representation and the representation of video's feature and obtained recommendation results in a controllable way to satisfy the diversity requirement of video recommendation. This paper conducted experiments on MovieLens-1M and TikTok datasets, and evaluated the model performance using accuracy, recall, NDCG and diversity metrics. The experimental results show that IMSGL-VRM has significant performance improvement compared with the classical benchmark model.

Foundation Support

国家自然科学基金资助项目(62266011)
贵州省科技计划资助项目(黔科合基础ZK［2022］一般119)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.11.0550
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 6
Section: Algorithm Research & Explore
Pages: 1679-1685
Serial Number: 1001-3695(2023)06-012-1679-07

Publish History

[2023-01-18] Accepted Paper
[2023-06-05] Printed Article

Cite This Article

余文婷, 吴云, 林建. 融合多模态自监督图学习的视频推荐模型 [J]. 计算机应用研究, 2023, 40 (6): 1679-1685. (Yu Wenting, Wu Yun, Lin Jian. Self-supervised graph learning of fusing multi-modal for video recommendation model [J]. Application Research of Computers, 2023, 40 (6): 1679-1685. )

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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