HyBLRecs: a hybrid drug recommendation system integrating content analysis and reliability enhancement

Drug recommendation systems (DRS) play a crucial role in personalized medicine. Traditional Collaborative Filtering (CF) methods often face challenges with data sparsity and cold-start problems due to their reliance on drug/user identifiers (IDs). To address these limitations, we propose HyBLRecs, a recommendation system leveraging content analysis and reliability enhancement. HyBLRecs eliminates the reliance on IDs, instead employing a three-branch hybrid network architecture to deeply mine content from patient reviews. The model integrates the semantic capabilities of BioBERT, sentiment analysis, and statistical features to train a high-performance LightGBM classifier (AUC ≈ 0.98). Finally, an efficient confidence-based ranking mechanism is applied to balance predictive efficacy, stability, and evidentiary reliability (N). Experimental results on the Drugs.com dataset demonstrate that HyBLRecs outperforms existing methods, achieving an F1 score of 96.91% and an overall accuracy of 95.63%, which confirms its strong generalizability even for unseen drugs.