A framework of a Personalized Location-based traveler recommendation system in mobile application

In this era of evolving technology, there are various channels and platforms through which travelers can find tour information and share their tour experience. These include tourism websites, social network sites, blogs, forums, and various search engines such as Google, Yahoo, etc. However, information found in this way is not filtered based on travelers' preferences. Hence, travelers face an information overflow problem. There is also increasing demand for more information on local area attractions, such as local food, shopping spots, places of interest and so on during the tour. The goal of this research is to propose a suitable recommendation method for use in a Personalized Location-based Traveler Recommender System (PLTRS) to provide personalized tourism information to its users. A comparative study of available recommender systems and location-based services (LBS) is conducted to explore the different approaches to recommender systems and LBS technology. The effectiveness of the system based on the proposed framework is tested using various scenarios which might be faced by users.