Location Based Recommendation System Using Deep Learning

In the rapidly growing tourism industry, the demand for personalized and contextually relevant recommendations is more significant than ever. This paper presents a new approach to a location-based recommendation system designed to give Points of Interest (POIs) to enhance tourism sector. We leverage the advance deep learning techniques including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The system processes vast dataset which consists of attributes such as location of POIs, Latitude, Longitude, user rating and many more to deliver personalized recommendations. Utilizing CNNs for their capability of feature extraction and LSTMs for their ability to retain sequential patterns, this system adapts dynamically to evolving user interests. The scope this research includes development, implementation and continuous evaluation of the proposed method using primary data. Additionally, the study explores the commericial possibility of this approach, offering insights on how to enhance the customer experience while recommending POIs of user's preference. The system improves recommendation accuracy as well as identifies local attractions, contributing to a more tailored travel experience for users.