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A Survey of Controllable Learning: Methods, Applications, and Challenges in Information Retrieval

A Survey of Controllable Learning: Methods, Applications, and Challenges in Information Retrieval

Controllable Learning: Methods, Applications, and Challenges in Information Retrieval

Definition and Importance of Controllable Learning

Controllable Learning (CL) ensures learning models meet predefined targets and adapt to changing requirements without retraining, enhancing reliability and effectiveness.

Taxonomy of Controllable Learning

The CL taxonomy categorizes who controls the learning process, what aspects are controllable, how control is implemented, and where power is applied.

User-Centric Control

User-centric control empowers users to shape their recommendation experience actively by modifying user profiles, interactions, and preferences.

Platform-Mediated Control

Platform-mediated control involves algorithmic adjustments and policy-based constraints imposed by the platform to enhance the recommendation process.

Implementation Techniques in Controllable Learning

Techniques include Rule-Based Techniques, Pareto Optimization, and Hypernetwork, which enhance adaptability and performance across various tasks and domains.

Applications in Information Retrieval

Controllable learning in IR ensures that learning models can dynamically adjust to different task descriptions, providing personalized and relevant search results without extensive retraining.

Conclusion

Controllable learning plays a critical role in ensuring trustworthy and adaptable machine learning systems, providing a comprehensive overview of methods, applications, and challenges.

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Vladimir Dyachkov, Ph.D
Editor-in-Chief itinai.com

I believe that AI is only as powerful as the human insight guiding it.

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