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LightFIG: simplifying and powering feature interactions via graph for recommendation.

Weiqiang Di1

  • 1School of Computer and Information Technology, Beijing Jiaotong University, Beijing, Beijing, China.

Peerj. Computer Science
|July 25, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces LightFIG, a recommendation model that simplifies attribute graphs and uses dual optimizers. This approach improves recommendation accuracy by focusing on user-item attribute relationships and efficient parameter optimization.

Keywords:
Attribute interactionsCollaborative filteringGraph neural networksRecommender systems

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • User and item attributes are crucial for effective recommendation systems.
  • Graph convolutional networks on attribute graphs enhance user-item representations.
  • Existing models often include within-attribute connections that can degrade performance and increase training time.

Purpose of the Study:

  • To propose an enhanced recommendation model, LightFIG, that addresses the limitations of current attribute graph construction and optimization strategies.
  • To improve recommendation accuracy and efficiency by focusing on cross-attribute relationships and employing advanced optimization techniques.

Main Methods:

  • LightFIG simplifies attribute graph construction, prioritizing relationships between user and item attributes over within-attribute relationships.
  • The model incorporates a novel 'relay optimization' strategy, utilizing two distinct optimizers for continuous parameter refinement.
  • Experiments were conducted on three public datasets to validate the model's effectiveness.

Main Results:

  • The simplified attribute graph construction in LightFIG effectively mines relevant user-item attribute relationships.
  • Relay optimization enhances the parameter tuning process, leading to better model performance.
  • Comprehensive experiments demonstrated significant improvements in recommendation quality compared to baseline models.

Conclusions:

  • LightFIG offers a more efficient and effective approach to attribute graph-based recommendation.
  • Focusing on cross-attribute relationships and employing dual optimizers are key to improving recommendation system performance.
  • The proposed model shows strong potential for practical application in recommendation systems.