Design and Implementation of a Learning Resource Recommendation System based on User Habits Based on GNN

Jingxuan Lu; YangKwon Jeong; Jiaqi Xue

doi:10.54097/fcis.v4i3.11135

Authors

Jingxuan Lu
YangKwon Jeong
Jiaqi Xue

DOI:

https://doi.org/10.54097/fcis.v4i3.11135

Keywords:

Graph Neural Networks, Personalized Recommendation, Data Sparsity

Abstract

This project aims to design and implement a learning resource recommendation system based on Graph Neural Networks (GNN). The system utilizes user learning habits as a foundation to provide personalized learning resource recommendations. By collecting and preprocessing user learning history data, and constructing a user-resource relationship graph, the GNN model is used to learn the representation vectors of users and resources. Combined with user habit features, appropriate recommendation algorithms are employed to recommend learning resources that align with their interests and habits.

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References

LEHMAN A,MILLER S J.A theoretical conversation about responses to information overload [J].Information,2020,11 ( 8) : 379-389.

ZHOU J, CUI G, ZHANG Z, et al.Graph neural networks a review of methods and applications[J]. arXiv: 1812. 08434, 2018.

VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need [C]/ /Advances in neural information processing systems.Long Beach: Curran Associates,Inc,2017: 6000-6010.

CHENG Z Y,DING Y,HE X N,et al.A^3NCF: an adaptive aspect attention model for rating prediction[C]/ /Proceedings of the 27th international joint conference on artificial intelligence. Louisiana: AAAI,2018: 3748-3754.

PEI W J,YANG J,SUN Z,et al.Interacting attention-gated recurrent networks for recommendatio [C]/ / Proceedings of the 2017 ACM on conference on information and knowledge management. Singapore: ACM,2017: 1459-14.