Social Media-Based Travel Planning: Integrating Content-Based and Collaborative Filtering for Personalized Suggestions

This research presents a data-driven approach for providing personalized travel and stay recommendations by integrating social media data., machine learning models., and time-series analysis. Data is collected from social media platforms using APIs., including textual data (captions., hashtags., and comments)., multimedia data (images and videos)., and engagement metrics (likes.,shares., and comments). Textual data is preprocessed using advanced NLP techniques such as BERT for keyword extraction and TextBlob for sentiment analysis., while multimedia data is classified using a pre-trained ResNet model. A comprehensive user travel history is built from geotags and timestamps., and seasonal travel patterns are analyzed using Prophet. User preferences are derived by combining engagement metrics., sentiment scores., and multimedia classification results. Unexplored destinations are identified using K-Means clustering., and similarity scores are computed using cosine similarity between user preferences and destination features. Recommendations for destinations and accommodations are generated using content-based filtering and collaborative filtering with Singular Value Decomposition (SVD). This integrated system ensures context-aware., relevant., and personalized travel and stay suggestions based on user interests and travel history.