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 users and items into a unified embedding space for matching assessment. These approaches have primary limitations, especially when dealing with explicit feedback and sparse data contexts. Two primary limitations are their proneness to overfitting and failure to incorporate epistemic uncertainty in predictions. To address these problems, we propose a novel Bayesian Deep Ensemble Collaborative Filtering method named BDECF. To improve model generalization and quality, we utilize Bayesian Neural Networks, which incorporate uncertainty within their weight parameters. In addition, we introduce a new interpretable non-linear matching approach for the user and item embeddings, leveraging the advantages of the attention mechanism. Furthermore, we endorse the implementation of an ensemble-based supermodel to generate more robust and reliable predictions, resulting in a more complete model. Empirical evaluation through extensive experiments and ablation studies across a range of publicly accessible real-world datasets with differing sparsity characteristics confirms our proposed method's effectiveness and the importance of its components.

本研究解决了现有推荐系统在面对显式反馈和稀疏数据时所面临的过拟合和未能考虑认知不确定性的问题。提出了一种名为BDECF的贝叶斯深度集成协同过滤方法，通过引入贝叶斯神经网络和可解释的非线性匹配机制来提高模型的泛化能力和预测可靠性，实验结果证实了该方法的有效性和组件的重要性。

基于贝叶斯深度集成学习的认知不确定性推荐系统