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Recommendation model based on generative adversarial network and social reconstruction.

Junhua Gu1,2, Xu Deng1, Ningjing Zhang3

  • 1School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300401, China.

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|June 16, 2023
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Summary
This summary is machine-generated.

This study introduces a novel recommendation model (SRGAN) that effectively uses social relations to combat data sparsity. It improves recommendations by selectively considering friends' opinions and optimizing social network structures.

Keywords:
dynamic reconfigurationgenerative adversarial networkgraph neural networkrecommendation algorithmsocial recommendation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Science

Background:

  • Data sparsity is a significant challenge in recommendation systems.
  • Existing social recommendation models often oversimplify the applicability and influence of social relations.
  • Current models fail to account for varying interaction scenarios and nuanced friend influences.

Purpose of the Study:

  • To propose a new recommendation model, Social Reconstruction Generative Adversarial Network (SRGAN), to address limitations in social recommendation.
  • To develop an adversarial framework that learns interactive data distribution more effectively.
  • To enhance the utilization of social relations for improved recommendation accuracy.

Main Methods:

  • Implemented a generative adversarial network (GAN) framework for learning user-item interaction data distribution.
  • Designed a generator that selects relevant friends based on user preferences and considers multi-angle friend influence.
  • Introduced a discriminator to differentiate between friends' opinions and users' personal preferences.
  • Incorporated a social reconstruction module to optimize social network structures and user relations.

Main Results:

  • The proposed SRGAN model demonstrated superior performance compared to existing social recommendation models.
  • Experimental validation on four datasets confirmed the model's effectiveness in leveraging social information.
  • The social reconstruction module successfully optimized social relations for better recommendation assistance.

Conclusions:

  • SRGAN effectively overcomes the limitations of existing social recommendation approaches.
  • The model's ability to selectively incorporate friend opinions and refine social networks leads to improved recommendations.
  • This work provides a more sophisticated method for utilizing social connections in recommendation systems.