research-article

Modeling Embedding Dimension Correlations via Convolutional Neural Collaborative Filtering

Authors:

Tat-Seng ChuaAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 37, Issue 4

Article No.: 47, Pages 1 - 22

https://doi.org/10.1145/3357154

Published: 13 September 2019 Publication History

Abstract

As the core of recommender systems, collaborative filtering (CF) models the affinity between a user and an item from historical user-item interactions, such as clicks, purchases, and so on. Benefiting from the strong representation power, neural networks have recently revolutionized the recommendation research, setting up a new standard for CF. However, existing neural recommender models do not explicitly consider the correlations among embedding dimensions, making them less effective in modeling the interaction function between users and items. In this work, we emphasize on modeling the correlations among embedding dimensions in neural networks to pursue higher effectiveness for CF. We propose a novel and general neural collaborative filtering framework—namely, ConvNCF, which is featured with two designs: (1) applying outer product on user embedding and item embedding to explicitly model the pairwise correlations between embedding dimensions, and (2) employing convolutional neural network above the outer product to learn the high-order correlations among embedding dimensions. To justify our proposal, we present three instantiations of ConvNCF by using different inputs to represent a user and conduct experiments on two real-world datasets. Extensive results verify the utility of modeling embedding dimension correlations with ConvNCF, which outperforms several competitive CF methods.

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Index Terms

Modeling Embedding Dimension Correlations via Convolutional Neural Collaborative Filtering
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. World Wide Web
    1. Web searching and information discovery
      1. Collaborative filtering

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 37, Issue 4

October 2019

299 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3357218

Editor:
Maarten de Rijke
University of Amsterdam, The Netherlands

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Copyright © 2019 ACM.

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Publication History

Published: 13 September 2019

Accepted: 01 August 2019

Revised: 01 June 2019

Received: 01 April 2019

Published in TOIS Volume 37, Issue 4

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National Natural Science Foundation of China
National Key Research and Development Program of China
National Science Foundation of China—Guangdong Joint Foundation
Natural Science Foundation of Jiangsu Province
National Research Foundation, Prime Minister's Office, Singapore, under its IRC@Singapore Funding Initiative

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https://dl.acm.org/doi/10.1145/3672758.3672844
Ruan SZhang KWu LXu TLiu QChen E(2024)Color Enhanced Cross Correlation Net for Image Sentiment AnalysisIEEE Transactions on Multimedia10.1109/TMM.2021.311820826(4097-4109)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2021.3118208
He QLi XCai B(2024)Graph neural network recommendation algorithm based on improved dual tower modelScientific Reports10.1038/s41598-024-54376-314:1Online publication date: 15-Feb-2024
https://doi.org/10.1038/s41598-024-54376-3
Feng Y(2023)Design and research of music teaching system based on virtual reality system in the context of education informatizationPLOS ONE10.1371/journal.pone.028533118:10(e0285331)Online publication date: 5-Oct-2023
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Gao YChen JXiao LWang HPan LWen XYe ZWu X(2023)Adversarial Neural Collaborative Filtering with Embedding Dimension CorrelationsData Intelligence10.1162/dint_a_001515:3(786-806)Online publication date: 1-Aug-2023
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