A Hybrid LSTM–Smith Waterman Model for Personalized Semantic Search in Academic Information Systems

The growing complexity of digital learning environments presents a critical challenge in computer science, particularly in designing intelligent academic systems capable of delivering context-aware and personalized content. Traditional academic information systems often rely on literal keyword matching, failing to interpret the semantic intent behind user queries and ignoring historical learning behavior. This study addresses these limitations by proposing a hybrid semantic search and recommendation model that integrates Long Short-Term Memory (LSTM) networks with the Smith Waterman algorithm. The LSTM component models temporal sequences of user interactions, while Smith Waterman enables local semantic alignment between user queries and learning content. Historical query logs and user-clicked topics are transformed into semantic vectors, which are further enhanced through a contextual graph and semantic relation matrix. Experimental results demonstrate the model’s effectiveness, achieving 89% accuracy, an F1-score of 0.89, and an AUROC of 0.88 by epoch 50. The hybrid architecture successfully captures the evolution of user interest and semantic relevance, outperforming baseline approaches. This research contributes to the field of computer science by bridging natural language understanding and sequential modeling to improve adaptive learning technologies. The proposed model offers a scalable foundation for developing intelligent recommendation systems in academic platforms, fostering improved learner engagement and efficiency.