Restricted Bernoulli Matrix Factorization: Balancing the trade-off between prediction accuracy and coverage in classification based collaborative filtering
arXiv:2210.10619v3 Announce Type: replace-cross Abstract: Reliability measures associated with the prediction of the machine learning models are critical to strengthening user confidence in artificial intelligence. Therefore, those models that provide not only predictions, but also reliability, enjoy greater popularity. In the field of recommender systems, reliability is crucial, since users tend to prefer those recommendations that are sure to interest them, that is, high predictions with high reliabilities. In this paper, we propose Restricted Bernoulli Matrix Factorization (ResBeMF), a new
The continuous evolution of AI models demands greater transparency and reliability to foster user trust, making research into explainable and reliable AI crucial at this stage of adoption.
This development addresses the critical challenge of AI model trustworthiness, vital for broader integration of machine learning in industries, particularly in recommender systems where user confidence directly impacts adoption and utility.
Machine learning models, especially in collaborative filtering, can now incorporate reliability measures directly into their predictive outcomes, shifting from mere prediction to prediction with confidence.
- · Recommender systems providers
- · E-commerce platforms
- · Users of AI-powered systems
- · AI ethics research
- · Black-box AI models
- · Systems prioritizing accuracy over transparency
Improved user satisfaction and engagement with AI-driven recommendations due to increased trust.
Accelerated adoption of AI in sensitive domains where reliability and explainability are paramount.
Potential for new regulatory frameworks emphasizing reliability and verifiability in AI systems, leading to a more standardized approach to AI development.
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Read at arXiv cs.AI