Tourism Recommender System Using Item-Based Hybrid Clustering Method (Case Study: Bandung Raya Region)

Abstract

The recommender system can be used to provide recommender for an item based on the highest. Therefore, the information recommended by the system can be as needed. The recommender system can help tourists to determine their travel choices, especially for tourists in the city of Bandung. In the recommender system there are two commonly used methods, namely collaborative filtering and content-based filtering methods. However, both methods still have drawbacks among them, in content-based filtering methods cannot recommend various items. While the collaborative filtering method cannot recommend items that have not been rated at all or cold start problems. The collaborative filtering method also cannot recommend to new users because new users do not have history. With the shortcomings of the two methods, the item-based clustering hybrid method (ICHM) is proposed to combine the two methods. The analysis was carried out by comparing the Mean Absolute Error (MAE) on several tests that have been carried out. In a cluster of 30 and c coefficient of 0.9, the average MAE value obtained is 0.2459 in cold start problems and 0.2488 in non cold problems. The smaller the MAE value is generated, that means the higher the level of accuracy.