Enhanced Hybrid Clustering and BAT-Optimized Deep U-Net for Next-Generation E-Commerce Product Recommendation

In the age of online commerce, product recommendation systems are a critical element in improving user experience and improving sales. In this study, the researcher suggests a powerful and smart product recommendation system that uses developed feature engineering, hybrid-clusters, deep learner, and ranking techniques to make effective recommendations. First, Multivariate Additive Independent Component Analysis (MAICA) is used to perform effective feature extraction, i.e., isolate independent latent variables of a complex high-dimensional dataset. Then, hybrid clustering algorithm based on the HAC and K-Means is executed to cluster similarity of product features and consumer interests and improve the accuracy and stability of the cluster. In order to further improve the feature set, Multi-Heuristic BAT Optimization Algorithm is introduced, which optimises the extracted features in order to achieve better learning results. A Deep Multi-layer U-Net Classifier is the core predictive model by which global and local patterns in data are captured to predict product relevance accurately. Lastly, the system uses the ranking-based recommendation strategy to rank and recommend the products that most suit the user intent and interests. The goal of this integrative approach is to be more successful than traditional recommendation methods in accuracy, scalability, and personalization. The relevance and accuracy of the recommendation has been established by experimental evidence and has proven useful in establishing the suggested system as a competitive solution in real-time, intelligent e-commerce recommendation. It is suggested that the following actions will be taken during the working process: adding user feedback loops and extending the framework to multilingual and multimodal data inputs.