Int-GNN: A User Intention Aware Graph Neural Network for Session-Based Recommendation

Guangning Xu; Jinyang Yang; Jinjin Guo; Zhichao Huang; Bowen Zhang

doi:10.1109/ICASSP49357.2023.10097031

Int-GNN: A User Intention Aware Graph Neural Network for Session-Based Recommendation

Guangning Xu, Jinyang Yang, Jinjin Guo, Zhichao Huang^*, Bowen Zhang^*

^*Corresponding author for this work

Department of Mathematics

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

8 Citations (Scopus)

Abstract

Session-Based Recommendation (SBR) is a spotlight research problem. Although many efforts have been made, challenges still exist. The key to unlocking this shackle is the user intention, an intuitive but hard-to-model concept in the anonymous session. Unlike previous research, we suggest mining potential user intention by counting the number of item occurrences in a user session and considering the long interval between item re-interactions. Beyond these, we take user preference, a biased user intention, into account in the prediction stage. Forming these together, we propose a model named user Intention aware Graph Neural Network (Int-GNN) aiming at capturing user intention. Extensive experiments have been conducted on three real-world datasets, and the results show the superiority of our method. The code is available on GitHub: https://github.com/xuguangning1218/IntGNN_ICASSP2023

Original language	English
Title of host publication	ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
Publisher	IEEE
Number of pages	5
ISBN (Electronic)	9781728163277
ISBN (Print)	9781728163284
DOIs	https://doi.org/10.1109/ICASSP49357.2023.10097031
Publication status	Published - 4 Jun 2023
Event	48th IEEE International Conference on Acoustics, Speech and Signal Processing - Rodos Palace Luxury Convention Resort, Rhodes Island, Greece Duration: 4 Jun 2023 → 10 Jun 2023 https://ieeexplore.ieee.org/xpl/conhome/10094559/proceeding (Conference proceeding) https://2023.ieeeicassp.org/ (Conference website) https://2023.ieeeicassp.org/program-at-a-glance/ (Conference program)

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2023-June
ISSN (Print)	1520-6149
ISSN (Electronic)	2379-190X

Conference

Conference	48th IEEE International Conference on Acoustics, Speech and Signal Processing
Abbreviated title	ICASSP 2023
Country/Territory	Greece
City	Rhodes Island
Period	4/06/23 → 10/06/23
Internet address	https://ieeexplore.ieee.org/xpl/conhome/10094559/proceeding (Conference proceeding) https://2023.ieeeicassp.org/ (Conference website) https://2023.ieeeicassp.org/program-at-a-glance/ (Conference program)

User-Defined Keywords

GNN
number of item occurrences
session based recommendation
user intention

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICASSP49357.2023.10097031

Cite this

Xu, G., Yang, J., Guo, J., Huang, Z., & Zhang, B. (2023). Int-GNN: A User Intention Aware Graph Neural Network for Session-Based Recommendation. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2023-June). IEEE. https://doi.org/10.1109/ICASSP49357.2023.10097031

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abstract = "Session-Based Recommendation (SBR) is a spotlight research problem. Although many efforts have been made, challenges still exist. The key to unlocking this shackle is the user intention, an intuitive but hard-to-model concept in the anonymous session. Unlike previous research, we suggest mining potential user intention by counting the number of item occurrences in a user session and considering the long interval between item re-interactions. Beyond these, we take user preference, a biased user intention, into account in the prediction stage. Forming these together, we propose a model named user Intention aware Graph Neural Network (Int-GNN) aiming at capturing user intention. Extensive experiments have been conducted on three real-world datasets, and the results show the superiority of our method. The code is available on GitHub: https://github.com/xuguangning1218/IntGNN_ICASSP2023",

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Xu, G, Yang, J, Guo, J, Huang, Z & Zhang, B 2023, Int-GNN: A User Intention Aware Graph Neural Network for Session-Based Recommendation. in ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2023-June, IEEE, 48th IEEE International Conference on Acoustics, Speech and Signal Processing, Rhodes Island, Greece, 4/06/23. https://doi.org/10.1109/ICASSP49357.2023.10097031

Int-GNN: A User Intention Aware Graph Neural Network for Session-Based Recommendation. / Xu, Guangning; Yang, Jinyang; Guo, Jinjin et al.
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings. IEEE, 2023. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2023-June).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Int-GNN: A User Intention Aware Graph Neural Network for Session-Based Recommendation

AU - Xu, Guangning

AU - Yang, Jinyang

AU - Guo, Jinjin

AU - Huang, Zhichao

AU - Zhang, Bowen

N1 - This research is supported by the Stable Support Project for Shenzhen Higher Education Institutions (SZWD2021011) and the Research Promotion Project of Key Construction Discipline in Guangdong Province (2022ZDJS112) Publisher Copyright: © 2023 IEEE.

PY - 2023/6/4

Y1 - 2023/6/4

N2 - Session-Based Recommendation (SBR) is a spotlight research problem. Although many efforts have been made, challenges still exist. The key to unlocking this shackle is the user intention, an intuitive but hard-to-model concept in the anonymous session. Unlike previous research, we suggest mining potential user intention by counting the number of item occurrences in a user session and considering the long interval between item re-interactions. Beyond these, we take user preference, a biased user intention, into account in the prediction stage. Forming these together, we propose a model named user Intention aware Graph Neural Network (Int-GNN) aiming at capturing user intention. Extensive experiments have been conducted on three real-world datasets, and the results show the superiority of our method. The code is available on GitHub: https://github.com/xuguangning1218/IntGNN_ICASSP2023

AB - Session-Based Recommendation (SBR) is a spotlight research problem. Although many efforts have been made, challenges still exist. The key to unlocking this shackle is the user intention, an intuitive but hard-to-model concept in the anonymous session. Unlike previous research, we suggest mining potential user intention by counting the number of item occurrences in a user session and considering the long interval between item re-interactions. Beyond these, we take user preference, a biased user intention, into account in the prediction stage. Forming these together, we propose a model named user Intention aware Graph Neural Network (Int-GNN) aiming at capturing user intention. Extensive experiments have been conducted on three real-world datasets, and the results show the superiority of our method. The code is available on GitHub: https://github.com/xuguangning1218/IntGNN_ICASSP2023

KW - GNN

KW - number of item occurrences

KW - session based recommendation

KW - user intention

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DO - 10.1109/ICASSP49357.2023.10097031

M3 - Conference proceeding

AN - SCOPUS:86000381386

SN - 9781728163284

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

BT - ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings

PB - IEEE

T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing

Y2 - 4 June 2023 through 10 June 2023

ER -

Xu G, Yang J, Guo J, Huang Z, Zhang B. Int-GNN: A User Intention Aware Graph Neural Network for Session-Based Recommendation. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings. IEEE. 2023. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP49357.2023.10097031

Int-GNN: A User Intention Aware Graph Neural Network for Session-Based Recommendation

Abstract

Publication series

Conference

User-Defined Keywords

UN SDGs

Access to Document

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Cite this