Proceedings of the 2023 International Conference on Applied Psychology and Modern Education (ICAPME 2023)

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Relation-aware Graph Convolutional Networks for Library Book Recommendation

Authors
Shuaishuai Fan1, Weiming Li1, *
1Shandong Technology and Business University, Yantai, 264000, China
*Corresponding author. Email: liweiming_xd@sdtbu.edu.cn
Corresponding Author
Weiming Li
Available Online 18 December 2023.
DOI
10.2991/978-2-38476-158-6_12How to use a DOI?
Keywords
Graph Convolutional Networks; Relational Attentive; Book Recommendation; Library
Abstract

In response to the issues of sparse borrowing relationship data, shifting content interests, and recommendation cold start in university library, this paper proposes a personalized book recommendation method based on Graph Convolutional Networks (GCNs). This method possesses conventional prediction and inductive capabilities by constructing a global tripartite graph of borrowers, books, and subjects and training a relation-aware GCN on local subgraphs. Additionally, a temporal self-attention (TSA) mechanism is proposed to encode long-term and short-term temporal patterns of borrower preferences, overcoming the variations and decay of interests over time. Experimental results conducted on two datasets demonstrate the advanced performance of our method, which is further validated through testing with real library data. Our proposed method effectively addresses the recommendation cold start problem, and places greater emphasis on the recent borrowing interests of readers, thereby improving the accuracy of recommendations.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 International Conference on Applied Psychology and Modern Education (ICAPME 2023)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
18 December 2023
ISBN
10.2991/978-2-38476-158-6_12
ISSN
2352-5398
DOI
10.2991/978-2-38476-158-6_12How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Shuaishuai Fan
AU  - Weiming Li
PY  - 2023
DA  - 2023/12/18
TI  - Relation-aware Graph Convolutional Networks for Library Book Recommendation
BT  - Proceedings of the 2023 International Conference on Applied Psychology and Modern Education (ICAPME 2023)
PB  - Atlantis Press
SP  - 79
EP  - 86
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-158-6_12
DO  - 10.2991/978-2-38476-158-6_12
ID  - Fan2023
ER  -