Design and Development of an Intelligent Digital Marketing System Using Hybrid Collaborative Filtering with Context-Aware Deep Neural Network

The fast growth of social media and e-commerce websites requires a smart recommendation system to increase user interaction and conversion rates. Current digital marketing platforms suffer from the limitation of low personalization accuracy, sparse data handling inability, and low adaptability to changing user interests. Classical methods like demographic-based and content-based filtering do not possess the ability to completely understand implicit behavioral patterns, resulting in non-optimal recommendations. To overcome these shortcomings, the suggested approach incorporates a Hybrid Collaborative Filtering with ContextAware Deep Neural Network (HCF-CADNN), which blends user-item interaction analysis with contextual features like time, location, and device type. The model utilizes an attention mechanism to dynamically weigh the importance of features and latent factor embedding for identifying high-dimensional patterns. Comparative experiments against baseline collaborative filtering, matrix factorization, and gradient boosting models show better performance with accuracy of 96.7% and F1-score of 95.8%, representing significant improvements in precision and recall. The mechanism proposed addresses higher recommendation relevance, adaptability for new users, and resistance to sparse data, proving to be very efficient for dynamic and large-scale digital marketing settings.