short-paper

Popularity-Aware Graph Social Recommendation for Fully Non-Interaction Users

Authors:

Nozomu Onodera,

Takahiro Ogawa,

Miki HaseyamaAuthors Info & Claims

MMAsia '22: Proceedings of the 4th ACM International Conference on Multimedia in Asia

Article No.: 30, Pages 1 - 5

https://doi.org/10.1145/3551626.3564969

Published: 13 December 2022 Publication History

Abstract

In this paper, we address a novel social recommendation for users who have no interactions with items (unobserved users). This task can provide many applications such as recommendations for cold-start users after the first sign-up and targeted advertising, thus, it seems to be extremely meaningful. However, existing social recommendation methods are unsuitable for this task since they assume that all users have interactions with items or cannot recommend more effectively than MostPopular recommendation. Towards this end, we propose Unobserved user-oriented Graph Social Recommendation (UGSR), which learns the preferences of unobserved users and provides richer recommendations than MostPopular recommendation. The popularity-aware graph convolutional network, which is carefully designed for this task, simultaneously considers some user-item interactions, social relations, and item popularity for the effective user and item modeling.

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Cited By

Junwei HXu QJiang YWang ZSun YHuang QCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)HGOE: Hybrid External and Internal Graph Outlier Exposure for Graph Out-of-Distribution DetectionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681118(1544-1553)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681118

Index Terms

Popularity-Aware Graph Social Recommendation for Fully Non-Interaction Users
1. Information systems
  1. World Wide Web
    1. Web searching and information discovery
      1. Social recommendation

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Published In

cover image ACM Conferences

MMAsia '22: Proceedings of the 4th ACM International Conference on Multimedia in Asia

December 2022

296 pages

ISBN:9781450394789

DOI:10.1145/3551626

Conference Chair:
Shuqiang Jiang
CASROLE@GENERAL CHAIR
,
General Chairs:
Kiyoharu Aizawa
The University of Tokyo
,
Phoebe Chen
La Trobe
,
Keiji Yanai
The University of Electro-Communications

Copyright © 2022 ACM.

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 December 2022

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JSPS KAKENHI

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MMAsia '22

Sponsor:

SIGMM

MMAsia '22: ACM Multimedia Asia

December 13 - 16, 2022

Tokyo, Japan

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Overall Acceptance Rate 59 of 204 submissions, 29%

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Cited By

Junwei HXu QJiang YWang ZSun YHuang QCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)HGOE: Hybrid External and Internal Graph Outlier Exposure for Graph Out-of-Distribution DetectionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681118(1544-1553)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681118

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