A data analysis and processing approach for a POI recommendation system

we focused on exploring novel methods and approaches in data analysis and processing for recommendation systems. To build our recommendation system, we established models for points of interest (POIs) and users. Our solution incorporated three key factors: sentiment analysis, user preferences, and ratings, culminating in the integration of the LightGCN model. The sentiment analysis factor played a crucial role in analyzing user reviews and predicting ratings. The user preference factor enabled us to recommend the most suitable POIs based on individual preferences and interests. The culmination of these factors, along with the preprocessing, filtering, and modelling of the POIs, led to the integration of the LightGCN model. During the experimentation phase, we utilized Yelp datasets to preprocess, filter, and model the POIs, incorporating sentiment analysis of reviews. The recommendation system, developed in the same environment, utilized the combined results of the three factors and the LightGCN model to provide improved POI recommendations for users.