Hybrid Recommendation System for Tourism

Abstract

This paper adopts item-based collaborative filtering to predict the interests of an active tourist by collecting preferences or taste information from a number of other tourists. Our proposed mechanism is able to predict a set recommended tourism places of elicited rating places (e.g., ratings of 1 through 5 stars) for the active tourist pre-traveling places. Furthermore, giving restriction of traveling factors, such as budge and time, the recommendation system will refine the exact set of tourism places by applying genetic algorithm mechanism. Finally, the system is based on minimum cost to schedule traveling path from a set of selected places by the using genetic algorithm approach. Our proposed hybrid recommendation algorithm focuses on the refining efficiency and provides multi-functional tourism information.