Collaborative filtering method based on graph neural network

Chaoyi Wang

doi:10.54254/2755-2721/6/20230710

Research Article

Open access

Published on 14 June 2023

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Wang,C. (2023). Collaborative filtering method based on graph neural network. Applied and Computational Engineering,6,1280-1286.

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Collaborative filtering method based on graph neural network

Chaoyi Wang *^,1,

¹ Shandong University

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/6/20230710

Abstract

An essential component of contemporary computer application technology is the recommender system. The collaborative filtering is one of RS's most crucial elements. acquiring knowledge about vector representations or, the model benefits from the combination of the graph neural network and model-based collaborative filtering since it can calculate the high-order connectivity in the item-user graph and perform better overall. This connectivity successfully and explicitly introduces the collaboration signal into the embedding process. Therefore, better embeddings also imply greater performance compared to more established collaborative filtering techniques, such as matrix factorization. The neural graph collaborative filtering (NGCF) algorithm will be primarily introduced in this article. In this paper, the performance of the NGCF algorithm is verified on several data sets, and the experimental results show that there is still room for improvement in the process of practical application. For instance, the NGCF algorithm is not appropriate for processing complicated data, and user cold start is an issue. This study offers a remedy for the difficulties the NGCF algorithm ran into in real-world use. Research on how to enhance the NGCF algorithm considering the issues will continue.

Keywords

collaborative filtering, recommendation, graph neural network.

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References

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Cite this article

Wang,C. (2023). Collaborative filtering method based on graph neural network. Applied and Computational Engineering,6,1280-1286.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

Conference website: http://www.confspml.org

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)

Conference date: 25 February 2023

Editor：Omer Burak Istanbullu

Series: Applied and Computational Engineering

Volume number: Vol.6

ISSN：2755-2721(Print) / 2755-273X(Online)

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