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cs.IR2025
FIRE: Faithful Interpretable Recommendation Explanations
S. M. F. Sani, Asal Meskin, Mohammad Amanlou +1
Natural language explanations in recommender systems are often framed as a review generation task, leveraging user reviews as ground-truth supervision. While convenient, this appro…
cs.IR2025
Epistemic Uncertainty-aware Recommendation Systems via Bayesian Deep Ensemble Learning
Radin Cheraghi, Amir Mohammad Mahfoozi, Sepehr Zolfaghari +3
Recommending items to users has long been a fundamental task, and studies have tried to improve it ever since. Most well-known models commonly employ representation learning to map…