This paper proposes TripSmart, a hybrid group travel recommendation system that integrates Content-Based Filtering (CBF) and Neural Collaborative Filtering (NCF) to enhance recommendation accuracy and personalization. The CBF component utilizes TF-IDF vectorization and cosine similarity to model semantic relationships between user preferences and destination attributes, while the NCF component leverages deep neural architectures to capture latent user-item interaction patterns. The proposed hybrid framework combines both approaches using a weighted scoring mechanism to generate optimized recommendations. The system incorporates contextual parameters such as budget constraints, geographical location, and group preferences to improve decision relevance. Experimental evaluation demonstrates superior performance of the hybrid model, achieving a precision of 0.886, recall of 0.863, F1-score of 0.874, and accuracy of 0.819. The results indicate significant improvements in recommendation diversity, cold-start handling, and overall system robustness compared to standalone models.

This paper presents TripSmart, a hybrid travel recommendation system that integrates Content-Based Filtering and Neural Collaborative Filtering to generate personalized group travel recommendations. The proposed approach effectively improves recommendation accuracy, relevance, and diversity by combining semantic analysis with deep learning-based user-item interaction modeling. The experimental results demonstrate that the hybrid model outperforms individual approaches, achieving higher precision, recall, F1-score, and accuracy. The system also addresses key challenges such as cold-start problems and limited personalization, making it suitable for real-world travel planning applications. Future work can focus on integrating advanced techniques such as Graph Neural Networks and real-time data processing to further enhance recommendation quality. Additionally, incorporating user feedback mechanisms, explainable AI, and integration with booking platforms can improve system scalability, transparency, and practical usability.

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