Search Recommendation for Travel and Tourism Using Collaborative Filtering and CNN Algorithm

Abstract— Travel and tourism websites play a crucial role in helping users find relevant information and plan their trips. However, users often face challenges in finding the right destinations and travel packages based on their preferences and budget. In this seminar paper, propose a search recommendation system that combines Collaborative Filtering and Deep Learning algorithms to provide a more personalized and accurate recommendation. The system uses the user's search history, click-through data, and purchase history to model the user-item interactions and make recommendations. The results of the proposed system show that it outperforms existing search recommendation systems in terms of accuracy and user satisfaction. This seminar paper provides a comprehensive overview of the current state-of-the-art in search recommendation systems for travel and tourism websites and highlights the potential of the proposed system to improve the user experience