Personalized Product Recommendation Using Collaborative Filtering and Matrix Factorization in Digital Marketing

This paper proposes an enhanced recommendation framework combining collaborative filtering with matrix factorization for personalized product recommendations in digital marketing. The framework introduces three key innovations: a temporal dynamics integration mechanism, a context-aware feature extraction method, and a hybrid recommendation algorithm. Through extensive experiments on a dataset of $\mathbf{1. 2}$ million user-item interactions, our approach demonstrates superior performance with a 17.3% improvement in F1-score (0.874) compared to state-of-the-art solutions. The system achieves an initial recommendation accuracy of 76.8% for new users, reducing the cold-start adaptation period to 5 interactions. The distributed architecture demonstrates $\mathbf{9 2 \%}$ scaling efficiency across nodes, supporting up to $\mathbf{1 0 0}$ million users with latency below $\mathbf{5 0 m s}$. Memory utilization is optimized to 2.1 GB during peak operation, representing a 45% reduction compared to existing solutions. Our framework maintains 99.99% availability under varying load conditions while providing consistent recommendation quality across different user segments and item categories. The meta-learning component shows particular effectiveness in rapidly adapting to user preferences, with recommendation relevance improving by 31.2% during the first three user interactions. Feature extraction efficiency has been optimized through dimensional reduction techniques, maintaining 94.7% of information content while reducing the feature space by 65%.