Recommender systems have become an important research area in past few years. They have been developed for a variety of domains, especially e-commerce. Recommender systems also can be applied in tourism industry to help tourists organizing their travel plans. Recommender systems can be developed by a variety of different techniques such as Content-Based filtering (CB), Collaborative Filtering (CF), and Demographic Filtering (DF). However, each method has its own advantages and disadvantages. For this reason, many previous researches used several mixed methods with an aim to reduce the disadvantages of using a single method and get more accurate recommendations.
In this research, we proposed a hybrid recommender system that combines the results of different recommendation methods using data mining techniques. Data mining technique is a method to dig out hidden knowledge and rules among the various items from large number of information and establish the relationship between model data attributes and categories in order to get more effective relationship model predictions.
The experimental results showed that the proposed hybrid recommendation method outperforms each individual recommendation method in terms of five evaluation metrics.
