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Related Concept Videos

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Related Experiment Videos

Autoencoder-based contrastive learning for next basket recommendation.

Ling Huang1, Zhe-Yuan Li1, Xiao-Dong Huang1

  • 1College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China.

Neural Networks : the Official Journal of the International Neural Network Society
|October 12, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces Autoencoder-based Contrastive learning for Next Basket Recommendation (AC-NBR) to improve prediction accuracy by addressing data sparsity. The novel approach enhances item embeddings for more robust next basket recommendations.

Keywords:
AutoencoderContrastive learningData augmentationNext basket recommendation

Related Experiment Videos

Area of Science:

  • E-commerce and Recommender Systems
  • Machine Learning
  • Data Mining

Background:

  • Next Basket Recommendation (NBR) predicts user's future purchases based on historical data.
  • Data sparsity is a major challenge in NBR, hindering accurate predictions.
  • Existing Contrastive Learning (CL) methods for NBR have limitations in embedding disruption and applicability.

Purpose of the Study:

  • To propose a novel model, Autoencoder-based Contrastive learning for Next Basket Recommendation (AC-NBR), to overcome data sparsity in NBR.
  • To enhance basket embedding quality and applicability for diverse NBR scenarios.
  • To improve the accuracy of predicting items in a user's next basket.

Main Methods:

  • Developed an AE-based Basket Augmentation module using an encoder-decoder structure with Gaussian noise for diverse positive pair generation.
  • Implemented an AE-based Contrastive Learning module to construct positive pairs from augmented baskets and initial embeddings.
  • Utilized a Gated Recurrent Unit (GRU) and Multi-Layer Perceptrons (MLPs) in the Next-Basket Predictor module for final item prediction.

Main Results:

  • The proposed AC-NBR model effectively addresses data sparsity challenges in Next Basket Recommendation.
  • AE-based augmentation preserves core basket information while enhancing embedding diversity and adaptability.
  • Comprehensive experiments on three real-world datasets validate the effectiveness of AC-NBR.

Conclusions:

  • AC-NBR offers a robust and applicable solution for Next Basket Recommendation by leveraging autoencoder-based contrastive learning.
  • The method provides improved embedding representations, leading to more accurate next basket predictions.
  • The findings suggest a promising direction for enhancing recommender systems facing data sparsity.