Enhancing Product Recommendations with BERT A Hybrid Approach Integrating Collaborative Filtering and NLP

In the contemporary world of online retailing, consumers are easily perplexed by the ocean of products available to them, thus calling for sophisticated personalized recommendation systems to accompany user experience and sales uplift. The current work describes a hybrid product recommendation strategy by integrating classical collaborative filtering techniques with state-of-the-art innovations in Natural Language Processing (NLP) using particularly the pre-trained BERT architecture. The work begins by addressing the issue of data sparsity in large datasets, efficiently mitigating it by filtering out inactive users, which significantly increases matrix density. Classical collaborative filtering techniques, i.e., KNNWithMeans and Singular Value Decomposition (SVD), were utilized and tested, for which the user-based KNN model exhibited the smallest Root Mean Square Error (RMSE). To further augment recommendation quality, the BERT architecture was fine-tuned on product title-product description pairs laboriously trained for predicting product-item similarity. Experimental results show that the BERT-augmented system consistently outperforms traditional models, achieving 92% accuracy in similarity classification and generating more semantically relevant recommendations. The research demonstrates the efficacy of complementarily integrating deep learning and NLP capabilities with traditional recommendation algorithms by formulating both precise and contextual-product-aware recommendations. These findings affirm the value of incorporating semantic similarity models into real-world recommendation engines.