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A Modular Adversarial Approach to Social Recommendation

Published: 03 November 2019 Publication History

Abstract

This paper proposes a novel framework to incorporate social regularization for item recommendation. Social regularization grounded in ideas of homophily and influence appears to capture latent user preferences. However, there are two key challenges: first, the importance of a specific social link depends on the context and second, a fundamental result states that we cannot disentangle homophily and influence from observational data to determine the effect of social inference. Thus we view the attribution problem as inherently adversarial where we examine two competing hypothesis---social influence and latent interests---to explain each purchase decision. We make two contributions. First, we propose a modular, adversarial framework that decouples the architectural choices for the recommender and social representation models, for social regularization. Second, we overcome degenerate solutions through an intuitive contextual weighting strategy, that supports an expressive attribution, to ensure informative social associations play a larger role in regularizing the learned user interest space. Our results indicate significant gains (5-10% relative Recall@K) over state-of-the-art baselines across multiple publicly available datasets.

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cover image ACM Conferences
CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management
November 2019
3373 pages
ISBN:9781450369763
DOI:10.1145/3357384
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Published: 03 November 2019

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Author Tags

  1. adversarial machine learning
  2. generative adversarial networks
  3. neural collaborative filtering
  4. social recommendation

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CIKM '19 Paper Acceptance Rate 202 of 1,031 submissions, 20%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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  • (2024)A Survey of Graph Neural Networks for Social Recommender SystemsACM Computing Surveys10.1145/366182156:10(1-34)Online publication date: 22-Jun-2024
  • (2024)DGCR: depth graph convolution recommendation algorithm integrating social and residualInternational Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023)10.1117/12.3025637(161)Online publication date: 28-Feb-2024
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  • (2023)Semi-supervised Adversarial Learning for Complementary Item RecommendationProceedings of the ACM Web Conference 202310.1145/3543507.3583462(1804-1812)Online publication date: 30-Apr-2023
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