research-article

FIRE: Fast Incremental Recommendation with Graph Signal Processing

Authors:

Ning GuAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 2360 - 2369

https://doi.org/10.1145/3485447.3512108

Published: 25 April 2022 Publication History

Abstract

Recommender systems are incremental in nature. Recent progresses in incremental recommendation rely on capturing the temporal dynamics of users/items from temporal interaction graphs, so that their user/item embeddings can evolve together with the graph structures. However, these methods are faced with two key challenges: 1) model training and/or updating are time-consuming and 2) new users/items cannot be effectively handled. To this end, we propose the fast incremental recommendation (FIRE) method from a graph signal processing perspective. FIRE is non-parametric which does not suffer from the time-consuming back-propagations as in previous learning-based methods, significantly improving the efficiency of model updating. In addition, we encode user/item temporal information and side information by designing new graph filters in FIRE, which can capture the temporal dynamics of users/items and address the cold-start issue for new users/items, respectively. Experimental studies on four popular datasets demonstrate that FIRE can improve the accuracy by a large margin and improve the model updating efficiency by at least 3X compared with the state-of-the-art incremental recommendation algorithms. The Code is available at https://github.com/Yaveng/FIRE.

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Cited By

Xia JLi DGu HLu TZhang PShang LGu NNejdl WAuer SKarras OCha MMoens MNajork M(2025)Oracle-guided Dynamic User Preference Modeling for Sequential RecommendationProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703542(363-372)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3703542
Tang HWu SSun XZeng JXu GLi Q(2024)TCGC: Temporal Collaboration-Aware Graph Co-Evolution Learning for Dynamic RecommendationACM Transactions on Information Systems10.1145/368747043:1(1-27)Online publication date: 26-Nov-2024
https://dl.acm.org/doi/10.1145/3687470
Wang LLin BZhang X(2024)Social Attribute Based Graph Signal Processing for Social RecommendationProceedings of the 3rd International Conference on Computer, Artificial Intelligence and Control Engineering10.1145/3672758.3672838(488-493)Online publication date: 26-Jan-2024
https://dl.acm.org/doi/10.1145/3672758.3672838
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cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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National Natural Science Foundation of China (NSFC)

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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

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Cited By

Xia JLi DGu HLu TZhang PShang LGu NNejdl WAuer SKarras OCha MMoens MNajork M(2025)Oracle-guided Dynamic User Preference Modeling for Sequential RecommendationProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703542(363-372)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3703542
Tang HWu SSun XZeng JXu GLi Q(2024)TCGC: Temporal Collaboration-Aware Graph Co-Evolution Learning for Dynamic RecommendationACM Transactions on Information Systems10.1145/368747043:1(1-27)Online publication date: 26-Nov-2024
https://dl.acm.org/doi/10.1145/3687470
Wang LLin BZhang X(2024)Social Attribute Based Graph Signal Processing for Social RecommendationProceedings of the 3rd International Conference on Computer, Artificial Intelligence and Control Engineering10.1145/3672758.3672838(488-493)Online publication date: 26-Jan-2024
https://dl.acm.org/doi/10.1145/3672758.3672838
Xia JLi DGu HLu TZhang PShang LGu NAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Neural Kalman Filtering for Robust Temporal RecommendationProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635837(836-845)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635837
Xia JLi DGu HLu TZhang PShang LGu NChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Hierarchical Graph Signal Processing for Collaborative FilteringProceedings of the ACM Web Conference 202410.1145/3589334.3645368(3229-3240)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645368
Yang LLuo ZZhang STeng FLi T(2024)Continual Learning for Smart City: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.344712336:12(7805-7824)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3447123
Xie BHu CHuang HYu JXia H(2024)DCI-PFGL: Decentralized Cross-Institutional Personalized Federated Graph Learning for IoT Service RecommendationIEEE Internet of Things Journal10.1109/JIOT.2023.334088011:8(13837-13850)Online publication date: 15-Apr-2024
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Cheng KYe JLu XSun LDu B(2024)Temporal Graph Network for continuous-time dynamic event sequenceKnowledge-Based Systems10.1016/j.knosys.2024.112452304:COnline publication date: 25-Nov-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.112452
Lian YZhang LSong C(2024)Target-driven user preference transferring recommendationExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121773238:PBOnline publication date: 27-Feb-2024
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Zhang JShi JDuan JRen Y(2024)Latent side-information dynamic augmentation for incremental recommendationKnowledge and Information Systems10.1007/s10115-024-02165-966:10(6051-6078)Online publication date: 26-Jun-2024
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