research-article

AutoGSR: Neural Architecture Search for Graph-based Session Recommendation

Authors:

Yihua HuangAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1694 - 1704

https://doi.org/10.1145/3477495.3531940

Published: 07 July 2022 Publication History

Abstract

Session-based recommendation aims to predict next click action (e.g., item) of anonymous users based on a fixed number of previous actions. Recently, Graph Neural Networks (GNNs) have shown superior performance in various applications. Inspired by the success of GNNs, tremendous endeavors have been devoted to introduce GNNs into session-based recommendation and have achieved significant results. Nevertheless, due to the highly diverse types of potential information in sessions, existing GNNs-based methods perform differently on different session datasets, leading to the need for efficient design of neural networks adapted to various session recommendation scenarios. To address this problem, we propose Automated neural architecture search for Graph-based Session Recommendation, namely AutoGSR, a framework that provides a practical and general solution to automatically find the optimal GNNs-based session recommendation model. In AutoGSR, we propose two novel GNN operations to build an expressive and compact search space. Building upon the search space, we employ a differentiable search algorithm to search for the optimal graph neural architecture. Furthermore, to consider all types of session information together, we propose to learn the item meta knowledge, which acts as a priori knowledge for guiding the optimization of final session representations. Comprehensive experiments on three real-world datasets demonstrate that AutoGSR is able to find effective neural architectures and achieve state-of-the-art results. To the best of our knowledge, we are the first to study the neural architecture search for the session-based recommendation.

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Cited By

ZENG BCHI JCHEN JXIE L(2024)Context enhanced multi-level attention model for session-based recommendationSCIENTIA SINICA Informationis10.1360/SSI-2023-010454:9(2116)Online publication date: 10-Sep-2024
https://doi.org/10.1360/SSI-2023-0104
Chen BZhao XWang YFan WGuo HTang R(2024)A Comprehensive Survey on Automated Machine Learning for RecommendationsACM Transactions on Recommender Systems10.1145/36301042:2(1-38)Online publication date: 10-Apr-2024
https://dl.acm.org/doi/10.1145/3630104
Sun ZLiu HQu XFeng KWang YOng YHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Large Language Models for Intent-Driven Session RecommendationsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657688(324-334)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657688
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Index Terms

AutoGSR: Neural Architecture Search for Graph-based Session Recommendation
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

General Chairs:
Enrique Amigo
UNED
,
Pablo Castells
UAM and Amazon
,
Julio Gonzalo
UNED
,
Program Chairs:
Ben Carterette
Spotify
,
J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

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SIGIR: ACM Special Interest Group on Information Retrieval

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Published: 07 July 2022

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Natural Science Foundation of Jiangsu Province
National Natural Science Foundation of China
Key Research and Development Program of Jiangsu Province
Open Research Projects of Zhejiang Lab

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SIGIR '22

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SIGIR

SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

ZENG BCHI JCHEN JXIE L(2024)Context enhanced multi-level attention model for session-based recommendationSCIENTIA SINICA Informationis10.1360/SSI-2023-010454:9(2116)Online publication date: 10-Sep-2024
https://doi.org/10.1360/SSI-2023-0104
Chen BZhao XWang YFan WGuo HTang R(2024)A Comprehensive Survey on Automated Machine Learning for RecommendationsACM Transactions on Recommender Systems10.1145/36301042:2(1-38)Online publication date: 10-Apr-2024
https://dl.acm.org/doi/10.1145/3630104
Sun ZLiu HQu XFeng KWang YOng YHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Large Language Models for Intent-Driven Session RecommendationsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657688(324-334)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657688
Peng DJi L(2024)SRM-TGAKnowledge-Based Systems10.1016/j.knosys.2024.111763294:COnline publication date: 21-Jun-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111763
An GSun JYang YSun F(2024)Enhancing Collaborative Information with Contrastive Learning for Session-based RecommendationInformation Processing & Management10.1016/j.ipm.2024.10373861:4(103738)Online publication date: Jul-2024
https://doi.org/10.1016/j.ipm.2024.103738
Peng BParthasarathy SNing X(2024)Intention enhanced mixed attentive model for session-based recommendationData Mining and Knowledge Discovery10.1007/s10618-024-01023-038:4(2032-2061)Online publication date: 3-Jun-2024
https://doi.org/10.1007/s10618-024-01023-0
Yang YSun JAn G(2024)Exploring multi-dimensional interests for session-based recommendationMultimedia Systems10.1007/s00530-024-01437-230:5Online publication date: 13-Aug-2024
https://doi.org/10.1007/s00530-024-01437-2
Wang QWu AYuan YWang YZhong GGao XYang C(2024)Noise-Resistant Graph Neural Networks for Session-Based RecommendationWeb and Big Data10.1007/978-981-97-7235-3_10(144-160)Online publication date: 28-Aug-2024
https://doi.org/10.1007/978-981-97-7235-3_10
Zhang XXu BMa FLi CLin YLin H(2023)Bi-preference Learning Heterogeneous Hypergraph Networks for Session-based RecommendationACM Transactions on Information Systems10.1145/363194042:3(1-28)Online publication date: 29-Dec-2023
https://dl.acm.org/doi/10.1145/3631940
Wang ZZhao HLiang FShi CFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Node-dependent Semantic Search over Heterogeneous Graph Neural NetworksProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614989(2646-2655)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614989
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