Click-through rate (CTR) prediction is an important issue in online advertising and recommendation systems. It is used to estimate the likelihood that a user will click on ads. The method used in traditional CTR prediction tasks is nothing more than the combination of feature engineering +LR/FM. This kind of work to improve the prediction effect through a large number of feature engineering is timeconsuming and laborious, and the construction process is not universal. Due to the sparse and highdimensional characteristics of data features, it is necessary to measure the importance of sparse features and obtain efficient feature interactions. A novel CTR model based on Mutual Information and Feature Interaction (MiFiNN) is proposed to measure feature importance and fine-grained feature interaction. To weigh the importance of sparse features, we use mutual information to find the correlation between each sparse feature and click behavior, and assign weights to features based on their contribution to the click results. The weighted features are interacted by combining the outer product with the inner product. The resulting feature interactions and the dense features set of the original input are taken as DNN inputs. We verify the proposed model on four datasets. In addition, several widely known models are introduced for comparison. The experimental results indicate the superiority of the model.

Click-through rate (CTR) prediction is a research point for measuring recommendation systems and calculating AD traffic. Existing studies have proved that deep learning performs very well in prediction tasks, but most of the existing studies are based on deterministic models, and there is a big gap in capturing uncertainty. Modeling uncertainty is a major challenge when using machine learning solutions to solve real-world problems in various domains. In order to quantify the uncertainty of the model and achieve accurate and reliable prediction results. This paper designs a CTR prediction framework combining feature selection and feature interaction. In this framework, a CTR prediction model based on Bayesian deep learning is proposed to quantify the uncertainty in the prediction model. On the squeeze network and DNN parallel prediction model framework, the approximate posterior parameter distribution of the model is obtained using the Monte Carlo dropout, and obtains the integrated prediction results. Epistemic and aleatoric uncertainty are defined and adopt information entropy to calculate the sum of the two kinds of uncertainties. Epistemic uncertainty could be measured by mutual information. Experimental results show that the model proposed is superior to other models in terms of prediction performance and has the ability to quantify uncertainty.

Although recommendation systems based on neural collaborative filtering (NCF) can achieve high rating accuracy, the point estimates they generate do not express prediction confidence. Existing variants that account for uncertainty, such as Monte Carlo dropout, deep ensemble models, and single-head Bayesian networks, can only capture epistemic uncertainty. We propose BayesNCF-v2, a single-model Bayesian neural collaborative filtering framework that combines a Bayes-by-Backprop output layer with a heteroscedastic head trained under the Gaussian NLL, thereby jointly learning aleatoric and epistemic uncertainty. Across five seeds and three datasets, compared to standard NCF, it reduces ECE by 25–73% and NLL by 7–20%, while its calibration performance shows no statistically significant difference from heteroscedastic MC dropout and approaches that of a heteroscedastic deep ensemble consisting of five models, despite having significantly fewer parameters. For cold-start users, the learned aleatoric variance increases, thereby reducing the ECE for cold-start users on three datasets. These results hold consistently across both random and temporal partitions, demonstrating that heteroscedastic aleatoric modeling is the main driver of calibration in these experiments.