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  1. Home
  3. Causality-aware Social Recommender System With Network Homophily Informed Multi-treatment Confounders.
  1. Home
  3. Causality-aware Social Recommender System With Network Homophily Informed Multi-treatment Confounders.

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Causality-aware Social Recommender System with Network Homophily Informed Multi-treatment Confounders.

Xin Zan1, Alexander Semenov1, Chao Wang2

  • 1Department of Industrial and Systems Engineering, University of Florida, Gainesville, 32611, FL, USA.

Information Sciences
|November 25, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel causality-aware social recommender system. By treating recommendations as a multiple causal inference problem, it enhances accuracy by deconfounding user preferences using social network structures.

Keywords:
00001111matrix factorizationmultiple causal inferencenetwork homophilysocial recommender systemsunobserved confounders

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Area of Science:

  • Computer Science
  • Machine Learning
  • Causal Inference

Background:

  • Recommender systems typically predict user preferences from observed ratings, but ignore causality where item exposure influences ratings.
  • Existing causal methods often overlook the complexity of multiple items and simultaneous inference in real-world scenarios.
  • Social network information, while valuable, confounds user preferences and complicates deconfounding in social recommender systems.

Purpose of the Study:

  • To frame recommendation as a multiple causal inference problem for improved accuracy.
  • To develop a causality-aware social recommender system that integrates social network structures.
  • To mitigate confounding bias in networked observational data for enhanced social recommendations.

Main Methods:

  • Framing recommendation as a multiple causal inference problem.
  • Incorporating social network structures with matrix factorization for deconfounding.
  • Utilizing network homophily within matrix factorization models via regularization.
  • Employing a proximal gradient-based optimization framework for efficient model estimation.

    • Main Results:

    • The proposed method effectively mitigates confounding bias by learning network-informed multi-treatment confounders.
    • Latent variables capture network structure, leading to improved rating prediction accuracy.
    • The proximal gradient optimization framework enhances computational efficiency and incorporates network constraints.

    Conclusions:

    • Treating recommendation as a multiple causal inference problem is crucial for accurate predictions.
    • Integrating social network homophily into matrix factorization improves deconfounding and recommendation quality.
    • The developed causality-aware social recommender offers a computationally efficient and effective approach for networked data.