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Graph-based Regularization on Embedding Layers for Recommendation

Authors:

Yan ZhangAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 39, Issue 1

Article No.: 2, Pages 1 - 27

https://doi.org/10.1145/3414067

Published: 05 September 2020 Publication History

Abstract

Neural networks have been extensively used in recommender systems. Embedding layers are not only necessary but also crucial for neural models in recommendation as a typical discrete task. In this article, we argue that the widely used l₂ regularization for normal neural layers (e.g., fully connected layers) is not ideal for embedding layers from the perspective of regularization theory in Reproducing Kernel Hilbert Space. More specifically, the l₂ regularization corresponds to the inner product and the distance in the Euclidean space where correlations between discrete objects (e.g., items) are not well captured. Inspired by this observation, we propose a graph-based regularization approach to serve as a counterpart of the l₂ regularization for embedding layers. The proposed regularization incurs almost no extra computational overhead especially when being trained with mini-batches. We also discuss its relationships to other approaches (namely, data augmentation, graph convolution, and joint learning) theoretically. We conducted extensive experiments on five publicly available datasets from various domains with two state-of-the-art recommendation models. Results show that given a kNN (k-nearest neighbor) graph constructed directly from training data without external information, the proposed approach significantly outperforms the l₂ regularization on all the datasets and achieves more notable improvements for long-tail users and items.

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Cited By

Zhang SJiang TKuang KFeng FYu JMa JZhao ZZhu JYang HChua TWu F(2023)SLED: Structure Learning based Denoising for RecommendationACM Transactions on Information Systems10.1145/361138542:2(1-31)Online publication date: 8-Nov-2023
https://dl.acm.org/doi/10.1145/3611385
Zhang QZhu HWang PChen EXiong H(2023)Hierarchical Wi-Fi Trajectory Embedding for Indoor User Mobility Pattern AnalysisProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35962377:2(1-21)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3596237
Han JLiu HLiu SChen XTan NChai HXiong HSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)iETA: A Robust and Scalable Incremental Learning Framework for Time-of-Arrival EstimationProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599842(4100-4111)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599842
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Index Terms

Graph-based Regularization on Embedding Layers for Recommendation
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Regularization
    2. Machine learning approaches
      1. Learning latent representations
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 39, Issue 1

January 2021

329 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3423044

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 05 September 2020

Accepted: 01 July 2020

Revised: 01 June 2020

Received: 01 January 2020

Published in TOIS Volume 39, Issue 1

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Cited By

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https://dl.acm.org/doi/10.1145/3611385
Zhang QZhu HWang PChen EXiong H(2023)Hierarchical Wi-Fi Trajectory Embedding for Indoor User Mobility Pattern AnalysisProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35962377:2(1-21)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3596237
Han JLiu HLiu SChen XTan NChai HXiong HSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)iETA: A Robust and Scalable Incremental Learning Framework for Time-of-Arrival EstimationProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599842(4100-4111)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599842
Wu DHe YLuo X(2023)A Graph-Incorporated Latent Factor Analysis Model for High-Dimensional and Sparse DataIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2023.329286611:4(907-917)Online publication date: Oct-2023
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Li WSitu YDing LChen YYang Q(2023)MOF-GRU: A MOFid-Aided Deep Learning Model for Predicting the Gas Separation Performance of Metal–Organic FrameworksACS Applied Materials & Interfaces10.1021/acsami.3c1179015:51(59887-59894)Online publication date: 12-Dec-2023
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Zhu TChen JWang ZWu D(2022)Regularized Framework on Heterogeneous Hypergraph Model for Personal RecommendationTheoretical Computer Science10.1007/978-981-19-8152-4_11(160-174)Online publication date: 10-Dec-2022
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da Silva ASpinosa E(2022)A Sequential Recommender System with Embeddings Based on GraphSage AggregatorsIntelligent Systems10.1007/978-3-031-21689-3_1(1-15)Online publication date: 28-Nov-2022
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He MHan TDing T(2022)Multilevel Feature Interaction Learning for Session-Based Recommendation via Graph Neural NetworksWeb Engineering10.1007/978-3-031-09917-5_3(31-46)Online publication date: 5-Jul-2022
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Duran PKaratzoglou AVitria JXin XArapakis I(2021)Graph Convolutional Embeddings for Recommender SystemsIEEE Access10.1109/ACCESS.2021.30966099(100173-100184)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3096609
Wu SSun FZhang WXie XCui B(undefined)Graph Neural Networks in Recommender Systems: A SurveyACM Computing Surveys10.1145/3535101
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