Leveraging Feature Interaction for Modeling User-Item Interaction in Recommender Systems

Soobeom Jang; Soomin Lee; Junha Park; Jong Seok Lee

doi:10.1109/SMC53992.2023.10394154

Leveraging Feature Interaction for Modeling User-Item Interaction in Recommender Systems

Soobeom Jang
, Soomin Lee
, Junha Park
, Jong Seok Lee

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Challenges in recent recommender systems include how to model high-order feature interaction and how to exploit user-item interaction, particularly for neural network-based recommender systems. While previous approaches have focused only on one aspect, this paper attempts to address both simultaneously by extracting augmented embeddings for users and items with feature interaction and modeling user-item interaction using graph neural networks. Real-world experimental results show that the proposed method outperforms state-of-the-art methods considering one type of interaction.

Original language	English
Title of host publication	2023 IEEE International Conference on Systems, Man, and Cybernetics
Subtitle of host publication	Improving the Quality of Life, SMC 2023 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5108-5113
Number of pages	6
ISBN (Electronic)	9798350337020
DOIs	https://doi.org/10.1109/SMC53992.2023.10394154
Publication status	Published - 2023
Event	2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023 - Hybrid, Honolulu, United States Duration: 2023 Oct 1 → 2023 Oct 4

Publication series

Name	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)	1062-922X

Conference

Conference	2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023
Country/Territory	United States
City	Hybrid, Honolulu
Period	23/10/1 → 23/10/4

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering
Control and Systems Engineering
Human-Computer Interaction

Access to Document

10.1109/SMC53992.2023.10394154

Cite this

Jang, S., Lee, S., Park, J., & Lee, J. S. (2023). Leveraging Feature Interaction for Modeling User-Item Interaction in Recommender Systems. In 2023 IEEE International Conference on Systems, Man, and Cybernetics: Improving the Quality of Life, SMC 2023 - Proceedings (pp. 5108-5113). (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC53992.2023.10394154

Jang, Soobeom ; Lee, Soomin ; Park, Junha et al. / Leveraging Feature Interaction for Modeling User-Item Interaction in Recommender Systems. 2023 IEEE International Conference on Systems, Man, and Cybernetics: Improving the Quality of Life, SMC 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 5108-5113 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics).

@inproceedings{46f6598e3da24d57ad37f9fc63121c88,

title = "Leveraging Feature Interaction for Modeling User-Item Interaction in Recommender Systems",

abstract = "Challenges in recent recommender systems include how to model high-order feature interaction and how to exploit user-item interaction, particularly for neural network-based recommender systems. While previous approaches have focused only on one aspect, this paper attempts to address both simultaneously by extracting augmented embeddings for users and items with feature interaction and modeling user-item interaction using graph neural networks. Real-world experimental results show that the proposed method outperforms state-of-the-art methods considering one type of interaction.",

keywords = "augmented embedding, graph neural networks, high-order feature interaction",

author = "Soobeom Jang and Soomin Lee and Junha Park and Lee, \{Jong Seok\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023 ; Conference date: 01-10-2023 Through 04-10-2023",

year = "2023",

doi = "10.1109/SMC53992.2023.10394154",

language = "English",

series = "Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "5108--5113",

booktitle = "2023 IEEE International Conference on Systems, Man, and Cybernetics",

address = "United States",

}

Jang, S, Lee, S, Park, J & Lee, JS 2023, Leveraging Feature Interaction for Modeling User-Item Interaction in Recommender Systems. in 2023 IEEE International Conference on Systems, Man, and Cybernetics: Improving the Quality of Life, SMC 2023 - Proceedings. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, Institute of Electrical and Electronics Engineers Inc., pp. 5108-5113, 2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023, Hybrid, Honolulu, United States, 23/10/1. https://doi.org/10.1109/SMC53992.2023.10394154

Leveraging Feature Interaction for Modeling User-Item Interaction in Recommender Systems. / Jang, Soobeom; Lee, Soomin; Park, Junha et al.
2023 IEEE International Conference on Systems, Man, and Cybernetics: Improving the Quality of Life, SMC 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. p. 5108-5113 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Leveraging Feature Interaction for Modeling User-Item Interaction in Recommender Systems

AU - Jang, Soobeom

AU - Lee, Soomin

AU - Park, Junha

AU - Lee, Jong Seok

PY - 2023

Y1 - 2023

N2 - Challenges in recent recommender systems include how to model high-order feature interaction and how to exploit user-item interaction, particularly for neural network-based recommender systems. While previous approaches have focused only on one aspect, this paper attempts to address both simultaneously by extracting augmented embeddings for users and items with feature interaction and modeling user-item interaction using graph neural networks. Real-world experimental results show that the proposed method outperforms state-of-the-art methods considering one type of interaction.

AB - Challenges in recent recommender systems include how to model high-order feature interaction and how to exploit user-item interaction, particularly for neural network-based recommender systems. While previous approaches have focused only on one aspect, this paper attempts to address both simultaneously by extracting augmented embeddings for users and items with feature interaction and modeling user-item interaction using graph neural networks. Real-world experimental results show that the proposed method outperforms state-of-the-art methods considering one type of interaction.

KW - augmented embedding

KW - graph neural networks

KW - high-order feature interaction

UR - https://www.scopus.com/pages/publications/85187254486

UR - https://www.scopus.com/pages/publications/85187254486#tab=citedBy

U2 - 10.1109/SMC53992.2023.10394154

DO - 10.1109/SMC53992.2023.10394154

M3 - Conference contribution

AN - SCOPUS:85187254486

T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

SP - 5108

EP - 5113

BT - 2023 IEEE International Conference on Systems, Man, and Cybernetics

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

Y2 - 1 October 2023 through 4 October 2023

ER -

Jang S, Lee S, Park J, Lee JS. Leveraging Feature Interaction for Modeling User-Item Interaction in Recommender Systems. In 2023 IEEE International Conference on Systems, Man, and Cybernetics: Improving the Quality of Life, SMC 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. p. 5108-5113. (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics). doi: 10.1109/SMC53992.2023.10394154

Leveraging Feature Interaction for Modeling User-Item Interaction in Recommender Systems

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