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HDGFI: Hierarchical Dual-Level Graph Feature Interaction Model for Personalized Recommendation.

Xinxin Ma1, Zhendong Cui1

  • 1School of Computer and Control Engineering, Yantai University, Yantai 264005, China.

Entropy (Basel, Switzerland)
|December 23, 2022
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Summary

Recommendation systems face challenges with feature interactions generating noise and lacking interpretability. The Hierarchical Dual-level Graph Feature Interaction (HDGFI) model improves accuracy by filtering noise and enhancing feature representation.

Keywords:
dual-level graph feature interactionsfeature interactiongraph structurepersonalized recommendation

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Area of Science:

  • Computer Science
  • Artificial Intelligence

Background:

  • Information overload necessitates effective recommendation systems.
  • Feature interaction is crucial but faces challenges with noise and interpretability.

Purpose of the Study:

  • To propose a novel model for feature interaction in recommendation systems.
  • To address noise generation and lack of interpretability in feature interactions.

Main Methods:

  • Introduced the Hierarchical Dual-level Graph Feature Interaction (HDGFI) model.
  • Utilized graph structures where features are nodes and interactions are edges.
  • Implemented a hierarchical edge selection module for beneficial interaction selection.
  • Modeled interaction importance from dual perspectives for finer granularity.

Main Results:

  • The HDGFI model effectively filters interaction noise.
  • The model enhances the interpretability of feature interactions.
  • Experimental results demonstrate higher accuracy compared to existing models.

Conclusions:

  • The HDGFI model offers a significant advancement in recommendation system feature interaction.
  • The proposed method improves accuracy and interpretability, addressing key limitations.