Apr 25, 2018 · In this paper, we propose a Confidence-aware Matrix Factorization (CMF) framework to simultaneously optimize the accuracy of rating prediction ...
The literature has reported that matrix factorization methods often produce superior accuracy of rating predic- tion in recommender systems. However, existing ...
This paper proposes a Confidence-aware Matrix Factorization (CMF) framework to simultaneously optimize the accuracy of rating prediction and measure the ...
Feb 2, 2018 · In this paper, we propose a Confidence-aware Matrix Factorization (CMF) framework to simultaneously optimize the accuracy of rating prediction ...
Sep 30, 2021 · A confidence matrix is explored and designed to measure the relationship between the rating outliers and misleading reviews, which helps improve ...
We propose two implementations, i.e., Confidence-aware Probabilistic Matrix. Factorization and Confidence-aware Bayesian Probabilistic Matrix. Factorization, ...
Abstract The main goal of a Recommender System is to suggest relevant items to users, although other utility dimensions – such as diversity, novelty, ...
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Mar 18, 2024 · By fine-tuning item confidence through CP-based losses, CPFT significantly enhances model performance, leading to more precise and trustworthy ...
Diverging from existing methods, we introduce a novel approach: Confidence-aware Fine-tuning of. Sequential Recommendation Systems via Conformal Prediction.
Aug 13, 2024 · This study leverages Bayesian theory and develops an uncertainty-aware online physician recommender system, including a Bayesian deep collaborative filtering ( ...