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Related Experiment Video

Updated: Jan 15, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
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A self-supervised group recommendation model with conformity awareness.

Yue Kou1, Dong Li2, Derong Shen1

  • 1School of Computer Science and Engineering, Northeastern University, Shenyang, 110167, Liaoning, China.

Scientific Reports
|October 15, 2025
PubMed
Summary
This summary is machine-generated.

We introduce ConfGR, a novel group recommendation model that accounts for conformity psychology. This approach enhances recommendation accuracy by integrating social selection and influence dynamics using multi-motif HyperGraph Convolutional Networks (HGCN).

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Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Social Computing

Background:

  • Group recommendation systems aim to satisfy multiple users' preferences.
  • Conformity psychology significantly impacts group recommendation quality.
  • Existing methods inadequately address conformity dynamics in group recommendations.

Purpose of the Study:

  • To propose a novel self-supervised model, ConfGR, for enhanced group recommendation.
  • To integrate social selection and social influence perspectives of conformity.
  • To improve the accuracy and interpretability of group recommendation systems.

Main Methods:

  • Developed a Conformity-aware Group Recommendation (ConfGR) model.
  • Utilized multi-motif HyperGraph Convolutional Networks (HGCN) to capture conformity.
  • Integrated social-motif, retail-motif, and groupon-motif HGCNs.
  • Employed self-supervised learning to optimize group embeddings.

Main Results:

  • ConfGR effectively integrates individual and group conformity dynamics.
  • The model dynamically captures conformity changes within and across groups.
  • Experimental results on three real-world datasets demonstrate superior performance over state-of-the-art methods.

Conclusions:

  • The proposed ConfGR model offers a significant advancement in group recommendation.
  • Accounting for conformity psychology enhances recommendation system performance.
  • ConfGR provides a robust framework for future research in personalized and group-aware recommendations.