CHEESE Net: A Feature-Optimized Hybrid Learning Model

Abstract

Intelligent cheese selection is critical in the dairy industry to address rising consumer demand for personalized nutrition and health-conscious choices. This study introduces the novel integration of supervised learning, unsupervised clustering, and deep learning autoencoders to dynamically optimize feature representation and recommendation quality, a previously unaddressed approach in dairy informatics. The system employs Random Forest Regression for caloric prediction, PCA for dimensionality reduction, and deep autoencoders to capture non-linear nutrition relationships. Recommendations are generated via cosine similarity and Euclidean distance, supported by clustering techniques to refine cheese categories. Cheese net achieved exceptional predictive accuracy with a Mean Absolute Error (MAE) of 14.46 and an R² Score of 0.98, outperforming traditional models. Advanced visualizations (heatmaps, t-SNE, PCA plots) uncovered latent nutritional patterns while clustering enhanced recommendation precision by aligning suggestions with user-specific dietary profiles. The hybrid model’s interpretability enables stakeholders to decode correlations between fat, protein, carbohydrates, and moisture content, facilitating data-driven decisions for producers and consumers. By unifying machine learning with explainable AI, Cheese Net reduces MAE by 31% compared to standalone regression models. This framework pioneers a scalable, data-driven solution for personalized cheese selection, bridging nutritional science and consumer needs in the digital dairy era.