Personalized Tourist Attraction Recommendation System Using Collaborative Filtering on Tourist Preferences

Abstract

A recommendation system becomes a good assistant in filtering various information from diverse sources to perform a matching result to users. These systems can provide a list of recommendations personalized to user preferences and needs. Almost any business can benefit from a recommendation system, including the tourism industry. In this paper, A personalized tourist attraction recommendation system (PTARS) based on a collaborative filtering technique is proposed. The research objective is to find the best model to recommend a customized destination to a new target user based on their preferences and behavior by using a user's travel-related data source acquired by an explicit approach. Our research result exhibits that the best similarity measure that yields the most accurate result is Euclidean distance; that calculation was from the top 25 k-neighbor values.