Context-Aware Hybrid Deep Learning for Intent Prediction in Imbalanced E-commerce Datasets

Modern e-commerce ecosystems generate massive volumes of high-velocity behavioral data, yet extracting actionable purchasing intent remains a significant challenge due to extreme class imbalance and the non-linear nature of human decision-making. In a typical retail environment, over 96% of digital footprints represent non-conversion events, creating approximately a 24:1 ratio that frequently leads to majority-class bias in traditional Machine Learning models. This investigation presents a mathematically grounded, Context-Aware Three-Tower Hybrid Deep Learning Architecture designed to unify disparate behavioral modalities into a single predictive framework. Tower 1 (Collaborative Domain) utilizes high-dimensional Embedding Layers to map visitor and item identifiers into a shared latent space. Tower 2 (Sequential Domain) processes the user's recent browsing trajectory using Global Average Pooling to synthesize short-term micro-intent. Tower 3 (Contextual Domain) integrates cyclical Sine and Cosine temporal embeddings to capture the periodic nature of shopping behavior. Standard Binary Cross-Entropy loss is replaced with Focal Loss (γ = 2.0, α = 0.25), which down-weights easily classified negative examples and focuses optimization on genuine purchasing signals. Experimental results on the 2.7 million-row Retailrocket dataset demonstrate a Validation AUC-ROC of 88.7%. An Ablation Study confirms synergistic uplift: the fully integrated Hybrid Engine achieved 36.83% prediction confidence, representing a substantial absolute gain over isolated Collaborative (29.31%) and Content-based (31.58%) baselines.