Developing Tourism Users’ Profiles with Data-Driven Explicit Information

In recommender systems (RSs), explicit information is often preferred over implicit because it is much more accurate than implicit or predicted information; for example, the user can enter information about his interests directly into the system, and the system will generate accurate recommendations for him. Receiving explicit information, however, may be difficult for a system. Explicit demographic information might be uncomfortable for some users, and extremely common questions, such as race, gender, income, and age, can lead to bias and unfair recommendations. As a result, in this study, we propose a method, in which information collected from a new user does not contain demographic information, and enquired explicit information is data driven. Users’ interest in tourism activities is used to identify seven categories of tourism. The mapping between extracted categories and activities is established with a multilabel classification (MLC) algorithm. The user’s interest in 18 tourism activities is predicted by rating only seven tourism categories. Common MLC algorithms with different classifiers were used to evaluate the proposed method. The best result relates to binary relevance with the Naïve Bayes classifier, which also outperforms the entitled algorithms in collaborative filtering (CF) systems as baseline models. The proposed method can capture users’ interests and develop their profiles without receiving demographic information. Also, compared to CF, in addition to a slight advantage in metrics, it only requires seven ratings to predict user interest in 18 activities. In contrast, CF algorithms require at least 15 user ng records to predict user interest in unknown activities (3-4 activities) to achieve a performance close to the proposed method.