short-paper

Non-Recursive Cluster-Scale Graph Interacted Model for Click-Through Rate Prediction

Authors:

Shengyuan Chen,

Feiran HuangAuthors Info & Claims

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Pages 3748 - 3752

https://doi.org/10.1145/3583780.3615180

Published: 21 October 2023 Publication History

Abstract

Extracting users' interests from their behavior, particularly their 1-hop neighbors, has been shown to enhance Click-Through Rate (CTR) prediction performance. However, online recommender systems impose strict constraints on the inference time of CTR models, which necessitates pruning or filtering users' 1-hop neighbors to reduce computational complexity. Furthermore, while the graph information of users and items has been proven effective in collaborative filtering models, recursive graph convolution can be computationally costly and expensive to implement. To address these challenges, we propose the Non-Recursive Cluster-scale Graph Interacted (NRCGI) model, which reorganizes graph convolutional networks in a non-recursive and cluster-scale view to enable CTR models to consider deep graph information with low computational cost. NRCGI employs non-recursive cluster-scale graph aggregation, which allows the online recommendation computational complexity to shrink from tens of thousands of items to tens to hundreds of clusters. Additionally, since NRCGI aggregates neighbors in a non-recursive view, each hop of neighbors has a clear physical meaning. NRCGI explicitly constructs meaningful interactions between the hops of neighbors of users and items to fully model users' intent towards the given item. Experimental results demonstrate that NRCGI outperforms state-of-the-art baselines in three public datasets and one industrial dataset while maintaining efficient inference.

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Cited By

Zhang YBei YChen HShen QYuan ZGong HWang SHuang FHuang XBaeza-Yates RBonchi F(2024)Multi-Behavior Collaborative Filtering with Partial Order Graph Convolutional NetworksProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671569(6257-6268)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671569
Chen HBei YShen QXu YZhou SHuang WHuang FWang SHuang XChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Macro Graph Neural Networks for Online Billion-Scale Recommender SystemsProceedings of the ACM Web Conference 202410.1145/3589334.3645517(3598-3608)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645517
Bei YXu HZhou SChi HWang HZhang MLi ZBu J(2024)CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00097(1199-1212)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00097
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Index Terms

Non-Recursive Cluster-Scale Graph Interacted Model for Click-Through Rate Prediction
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Social recommendation
2. Information systems
  1. Information systems applications
    1. Computational advertising

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cover image ACM Conferences

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

October 2023

5508 pages

ISBN:9798400701245

DOI:10.1145/3583780

General Chairs:
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University of Wolverhampton, UK
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University of Birmingham, UK
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University of Birmingham, UK
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Program Chairs:
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Spotify, UK
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Tsinghua University, China
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Federal University of Minas Gerais, Brazil

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Published: 21 October 2023

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Zhejiang Provincial Natural Science Foundation of China
Ningbo Natural Science Foundation
National Natural Science Foundation of China

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CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management

October 21 - 25, 2023

Birmingham, United Kingdom

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Cited By

Zhang YBei YChen HShen QYuan ZGong HWang SHuang FHuang XBaeza-Yates RBonchi F(2024)Multi-Behavior Collaborative Filtering with Partial Order Graph Convolutional NetworksProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671569(6257-6268)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671569
Chen HBei YShen QXu YZhou SHuang WHuang FWang SHuang XChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Macro Graph Neural Networks for Online Billion-Scale Recommender SystemsProceedings of the ACM Web Conference 202410.1145/3589334.3645517(3598-3608)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645517
Bei YXu HZhou SChi HWang HZhang MLi ZBu J(2024)CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00097(1199-1212)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00097
Zhang YBei YYang SChen HLi ZChen LHuang F(2023)Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering2023 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW60847.2023.00075(528-532)Online publication date: 4-Dec-2023
https://doi.org/10.1109/ICDMW60847.2023.00075

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