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PlatoGL: Effective and Scalable Deep Graph Learning System for Graph-enhanced Real-Time Recommendation

Published: 17 October 2022 Publication History

Abstract

Recently, graph neural network (GNN) approaches have received huge interests in recommendation tasks due to their ability of learning more effective user and item representations. However, existing GNN-based recommendation models cannot support real-time recommendation where the model keeps its freshness by continuously training the streaming data that users produced, leading to negative impact on recommendation performance. To fully support graph-enhanced large-scale recommendation in real-time scenarios, a deep graph learning system is required to dynamically store the streaming data as a graph structure and enable the development of any GNN model incorporated with the capabilities of real-time training and online inference. However, such requirements rule out existing deep graph learning solutions. In this paper, we propose a new deep graph learning system called PlatoGL, where (1) an effective block-based graph storage is designed with non-trivial insertion/deletion mechanism for updating the graph topology in-milliseconds, (2) a non-trivial multi-blocks neighbour sampling method is proposed for efficient graph query, and (3) a cache technique is exploited to improve the storage stability. We have deployed PlatoGL in Wechat, and leveraged its capability in various content recommendation scenarios including live-streaming, article and micro-video. Comprehensive experiments on both deployment performance and benchmark performance~(w.r.t. its key features) demonstrate its effectiveness and scalability. One real-time GNN-based model, developed with PlatoGL, now serves the major online traffic in WeChat live-streaming recommendation scenario.

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Presentation video of CIKM2022 paper: PlatoGL, a deep graph learning system for graph-enhanced real-time recommendation

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Cited By

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  • (2024)λGrapher: A Resource-Efficient Serverless System for GNN Serving through Graph SharingProceedings of the ACM Web Conference 202410.1145/3589334.3645383(2826-2835)Online publication date: 13-May-2024
  • (2024)PlatoD2GL: An Efficient Dynamic Deep Graph Learning System for Graph Neural Network Training on Billion-Scale Graphs2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00191(2421-2434)Online publication date: 13-May-2024
  • (2024) E 2 GCL: Efficient and Expressive Contrastive Learning on Graph Neural Networks 2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00071(859-873)Online publication date: 13-May-2024
  • Show More Cited By

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cover image ACM Conferences
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management
October 2022
5274 pages
ISBN:9781450392365
DOI:10.1145/3511808
  • General Chairs:
  • Mohammad Al Hasan,
  • Li Xiong
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 17 October 2022

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Author Tags

  1. deep graph learning system
  2. graph neural network
  3. real-time recommendation

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CIKM '22 Paper Acceptance Rate 621 of 2,257 submissions, 28%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

View all
  • (2024)λGrapher: A Resource-Efficient Serverless System for GNN Serving through Graph SharingProceedings of the ACM Web Conference 202410.1145/3589334.3645383(2826-2835)Online publication date: 13-May-2024
  • (2024)PlatoD2GL: An Efficient Dynamic Deep Graph Learning System for Graph Neural Network Training on Billion-Scale Graphs2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00191(2421-2434)Online publication date: 13-May-2024
  • (2024) E 2 GCL: Efficient and Expressive Contrastive Learning on Graph Neural Networks 2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00071(859-873)Online publication date: 13-May-2024
  • (2023)EmbedX: A Versatile, Efficient and Scalable Platform to Embed Both Graphs and High-Dimensional Sparse DataProceedings of the VLDB Endowment10.14778/3611540.361154616:12(3543-3556)Online publication date: 1-Aug-2023
  • (2023)Dual Interests-Aligned Graph Auto-Encoders for Cross-domain Recommendation in WeChatProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614676(4988-4994)Online publication date: 21-Oct-2023

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