Personalized Recommendation System Using Deep Learning with Bayesian Personalized Ranking

Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories. The collaborative filtering (CF) model, which depends exclusively on user-item interactions, commonly encounters challenges, including the cold-start problem and an inability to effectively capture the sequential and temporal characteristics of user behavior. This paper introduces a personalized recommendation system that combines deep learning techniques with Bayesian Personalized Ranking (BPR) optimization to address these limitations. With the strong support of Long Short-Term Memory (LSTM) networks, we apply it to identify sequential dependencies of user behavior and then incorporate an attention mechanism to improve the prioritization of relevant items, thereby enhancing recommendations based on the hybrid feedback of the user and its interaction patterns. The proposed system is empirically evaluated using publicly available datasets from movie and music, and we evaluate the performance against standard recommendation models, including Popularity, BPR, ItemKNN, FPMC, LightGCN, GRU4Rec, NARM, SASRec, and BERT4Rec. The results demonstrate that our proposed framework consistently achieves high outcomes in terms of HitRate, NDCG, MRR, and Precision at K = 100, with scores of (0.6763, 0.1892, 0.0796, 0.0068) on MovieLens-100K, (0.6826, 0.1920, 0.0813, 0.0068) on MovieLens-1M, and (0.7937, 0.3701, 0.2756, 0.0078) on Last.fm. The results show an average improvement of around 15% across all metrics compared to existing sequence models, proving that our framework ranks and recommends items more accurately.