Hybrid Recommendation System for Tourism using Deep Learning

Abstract

Recommendation systems have become increasingly important in the tourism industry as they assist travelers in making informed decisions about their trips. Traditional recommendation systems use either collaborative filtering or content-based approaches to provide recommendations, which have certain limitations in terms of accuracy and personalization. In this poster, we present a hybrid recommendation system that combines both approaches using deep learning algorithms. Our system uses convolutional neural networks (CNN) to analyze and classify images of tourist destinations, and recurrent neural networks (RNN) to analyze user-generated content such as reviews and ratings. The system then combines the results of both approaches using a hybrid model that considers both item and user similarities. The model is designed to learn from user feedback and adapt to new trends and changes in the tourism industry. We evaluate our system using real-world data and compare it to traditional recommendation systems. Our results demonstrate that the proposed system provides more accurate and personalized recommendations compared to traditional approaches. We show that our system improves user satisfaction and engagement in the tourism industry. Overall, our hybrid recommendation system provides an innovative approach to solving the limitations of traditional approaches in the tourism industry. The system has the potential to improve user experience and engagement, leading to increased revenue for tourism businesses.