Abstract

Recommendation systems frequently suffer from noisy and unreliable data, which undermines prediction accuracy and system robustness. In this paper, we propose a novel framework that enhances data reliability in recommendation systems by integrating Named Entity Recognition (NER) with uncertainty quantification. Experimental evaluations demonstrate that our data-reliability enhanced prediction method significantly reduces erroneous recommendations and improves predictive confidence across diverse real-world benchmarks.