Content Filtering Based Navigation Recommendation System Using NLP

The surge in tourism information necessitates intelligent systems for efficient decision-making during trip planning. This paper introduces a Context-Aware Navigation Recommen- dation System (CANRS) using Natural Language Processing (NLP) and content filtering. The focus is on improving relevance and personalization by extracting insights from textual data. The framework integrates collaborative filtering, Content-Based Filtering, and NLP, analyzing textual data from social media, reviews, and descriptions for nuanced user preferences. The system utilizes TF-IDF, sentiment analysis, and semantic analysis for precise feature extraction, facilitating personalized travel itineraries. The paper introduces an optimization algorithm refining content filtering, considering location, user history, and real-time events for dynamic recommendation scores. Through experiments, the CANRS demonstrates accuracy and contextually relevant navigation recommendations, adapting to changing user preferences and dynamic contexts. This research leverages NLP to enhance content filtering, providing personalized and contextually aware recommendations. Implications include the development of advanced navigation systems prioritizing user preferences and dynamic contextual information in tourism.