Developing Tourism Users’ Profiles with Data-Driven Explicit Information

Abstract

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.

How can tourism managers' happiness be generated through personal and innovative tourism services?

“…Among the hypotheses, the high significance of hypothesis H4 stands out. IPTS positively affects OF as tourism managers seek to base their knowledge and algorithms on data to offer tailored suggestions based on smart-personalised tourism (Norouzi et al ., 2022). These services affect the organisation's structure (Um and Chung, 2021) and the type of personalised customer experience (Logesh et al ., 2019).…”

“…The literature on organisational innovation shows that in order to improve the delivery of tourism services to their users (Bošnjak et al ., 2017; Logesh et al ., 2019), firms have at their disposal management models that associate this term with the three Ps: prosperity, productivity and progress (Breznitz, 2021). All this can be achieved when companies promote internal knowledge sharing and innovative performance of their human resources (Um and Chung, 2021; Mariani et al ., 2023; Norouzi et al ., 2022).…”

ExhibitXplorer: Enabling Personalized Content Delivery in Museums Using Contextual Geofencing and Artificial Intelligence

“…Explicit profiling mainly uses questionnaires, which can be implemented both at the entrance and exit of the museum [29]. At the current stage, questionnaires are mostly conducted electronically through a mobile application, part of a personalized content delivery application.…”

ExhibitXplorer: Enabling Personalized Content Delivery in Museums using Contextual Geofencing and Artificial Intelligence

“…Explicit profiling mainly uses questionnaires, which can be implemented both at the entrance and exit of the museum [14]. At the current stage, polling is mostly conducted electronically through a mobile application, part of a personalized content delivery application.…”