Mobile tourism or m-tourism can assist and help tourists anywhere and anytime face the overload of information that may appear in their smartphones. Indeed, these mobile users find difficulties in the choice of points of interest (POIs) that may interest them during their discovery of a new environment (a city, a university campus ...). In order to reduce the number of POIs to visit, the recommendation systems (RS) represent a good solution to guide each tourist towards personalized paths close to his instantaneous location during his visit. In this article, we focus on (1) the detection of the spatiotemporal context of the tourist to filter the POIs and (2) the use of the previous notations of the places. These two criteria make it possible to integrate the evolutionary context of the visit in order to predict incrementally the most relevant transitions to be borrowed by the tourists without profile. These predictions are calculated using collaborative filtering algorithms that require the collection of traces of tourists to better refine the recommendation of POIs. In our software prototype, we have adapted the SLOPE ONE algorithm to our context of discovering the city of Chlef to avoid problems like data scarcity, cold start and scalability. In order to validate the use of this prototype, we conducted experiments by tourists in order to calculate indicators to assess the relevance of the recommendations provided by our system.

The objective of this article is to make a bibliographic study on the recommendation of learning activities that can integrate user mobility. This type of recommendation makes it possible to exploit the history of previous visits in order to offer adaptive learning according to the instantaneous position of the learner and the pedagogy of the guide. To achieve this objective, we review the existing literature on the recommendation systems that integrate contexts such as geographic location and training profile. Next, we are interested in the social relationships that users can have between themselves. Finally, we focus on the work of recommending mobile learning activities in the context of scenarios of field trips.

This paper presents a new approach to a recommendation of learning activities adapted to the spatial and temporal context of each mobile learner. Indeed, the path traveled by the user during a field trip can be guided using the technique of passive collaborative filtering. This recommendation is based on the ACO (Ant Colony Optimization) algorithm, which represents a good model for swarm intelligence. For this reason, the structure of our mobile scenario is described as a graph where POIs (Point Of Interest) are represented by nodes and the arcs indicate the probability of moving between them. This recommendation system allows the orchestration of mobile learning according to the geographical location of learners and the historical of their activities. Our contribution is devised in three parts: (1) the creation of a mobile learning scenario based on POIs, (2) the adaptation of the ACO algorithm for the orchestration of paths taken by learners, and (3) the development of a recommender system that helps learners to better choose their paths during the field trip.