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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Related Experiment Video

Updated: Jun 27, 2025

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
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Fairness-aware recommendation with meta learning.

Hyeji Oh1, Chulyun Kim2

  • 1Department of IT Engineering, Sookmyung Women's University, 100 Cheongpa-ro 47-gil, Yongsan-gu, Seoul, 04310, Korea.

Scientific Reports
|May 2, 2024
PubMed
Summary

This study introduces FaRM, a novel framework for fair recommendations in cold-start scenarios. FaRM enhances fairness for new users and items by using meta-learning, improving upon existing methods.

Keywords:
Artificial intelligenceCold-start recommendationDeep learningFairnessMeta-learningRecommender systems

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Information Retrieval

Background:

  • Fairness is a critical consideration in online systems, particularly recommender systems, which control item visibility.
  • Existing fairness-aware recommender systems often require substantial user-item interaction data, limiting their effectiveness in cold-start scenarios with new users and items.
  • The challenge of ensuring fairness is amplified when user preferences and item popularity are unknown due to a lack of historical data.

Purpose of the Study:

  • To develop and evaluate a novel framework, FaRM (Fairness-aware meta-learning Recommendation), designed to enhance recommendation fairness specifically within cold-start environments.
  • To address the limitations of previous approaches by proposing methods effective even with sparse or non-existent user-item relationship data.
  • To investigate and mitigate unfairness arising from unknown user preferences and item popularity.

Main Methods:

  • Proposed a meta-learning-based cold-start recommendation framework (FaRM).
  • Introduced a fairness-aware meta-path generation method to mitigate bias related to sensitive attributes.
  • Developed fairness-aware user representations via a meta-path aggregation approach.
  • Designed a novel fairness objective function and a joint learning method to balance relevancy and fairness.

Main Results:

  • FaRM demonstrated significantly superior fairness performance across various cold-start scenarios compared to existing methods.
  • The framework successfully preserved recommendation relevance accuracy while enhancing fairness.
  • Experimental results validate the effectiveness of FaRM in mitigating unfairness in data-scarce recommendation settings.

Conclusions:

  • The proposed FaRM framework effectively addresses fairness challenges in cold-start recommendation systems.
  • Meta-learning provides a viable approach to improve fairness when historical data is limited.
  • FaRM offers a promising solution for creating more equitable online recommendation experiences.