Using geographic information systems (GIS) widely for dealing with transportation problems (is well-known as GIS-T), has made it nessasary for researchers to discover the current state-of-the-art and predict the trends of future research. This paper aims to contribute to a better understanding of GIS-T research area from a longitudinal perspective, over the period 2008–2019. A co-word analysis was used to illustrate all the underlying subfields of GIS-T based on published papers in the Web of Science (WoS) database service. The main knowledge areas representing the intellectual structure of GIS-T including (a) sustainability, (b) health, (c) planning and management, and (d) methods and tools, were detected. Finally, in order to illustrate the structure and development of the identified clusters, two-dimensional maps and strategic diagrams for each period were drawn. This study is the first attempt to employ a text mining method so as to detect the conceptual structure of GIS-T research area from a complex and interdisciplinary literature.

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.

Background and Aim: The existence of an intellectual structure for every field is essential for managers and scholars. Intellectual structures provide a comprehensive map of knowledge that can guide researchers and managers to have a better view of their fields. Besides, with high-speed and massive amounts of data and information generation, reading and surveying of all resources are severely tricky. Intellectual maps solve this problem and make a situation for control and monitoring this voluminous and high-speed generated data. Epidemiology is regarded as one of the exciting fields which many researchers focused on it. A study of the structure and criteria of different epidemiological fields has not been done yet. Indeed, there is no serious effort for knowledge discovery of hidden information on epidemiological texts. Methods: In this paper, in order to survey this field, an intellectual structure is provided using co-word analysis. Utilizing co-word analysis discloses relationships and structure among research subjects and topics in a field. Results: Finally, four main clusters were determined, namely: genetic (with 30.53% of surveyed papers), illness (29.47%), modeling (23.16%), and prevention (16.84%). Conclusion: According to epidemiology co-word network, epidemiology area has not been studied from enough different areas, especially from novel technologies.