Temporal Dynamics in Popularity-Aware Web API Recommendation System

The surge in web APIs has revolutionized software development but navigating their abundance remains a challenge. While existing recommendation systems consider popularity and diversity, they lack a temporal dimension for anticipating future trends. This research introduces a novel approach by integrating temporal dynamics into web API recommendations. Utilizing Long Short-Term Memory (LSTM) for time series forecasting, this model goes beyond static awareness, providing developers with forward-looking insights. The methodology spans data collection, preprocessing, LSTM model training, and recommendation system integration. This evaluation highlights the system's effectiveness in predicting future popularity trends, empowering developers to proactively adopt emerging technologies. The fusion of temporal dynamics and LSTM forecasting propels web API recommendation systems into a more anticipatory realm, fostering innovation in software development.