Towards an Incremental Recommendation of POIs for Mobile Tourists without Profiles

Abstract

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.

Scoping out urban areas of tourist interest though geolocated social media data: Bucharest as a case study

“…The notion of areas of interest (AOIs) has emerged in addition to these POIs. AOIs integrate multiple scenic elements (Dennouni et al 2018 ; Mai et al 2018 ) and commonly agreed AOI features include: (1) the existence of a distinguishable core place related to a landmark—e.g., the ‘Central-Market’ area—; (2) the magnitude of its extension; or (3) its recognition as a whole. Such features, however, strongly vary according to users’ perceptions or experiences (Bennett and Agarwal 2007 ).…”

Recommendation of Heterogeneous Cultural Heritage Objects for the Promotion of Tourism

“…Collaborative filtering approaches use information about other users with similar characteristics (e.g., age, socio-professional activities, visit evaluation feedback) to recommend POIs to a given user. Several techniques are used by these approaches, such as clustering techniques, allowing, for example, the grouping of users according to their profile [23] or according to their friendship links on social networks, or even ant colony algorithms dedicated to the calculation of traces of pheromones left by users [24]. In the latter case, the recommendation considers only the popularity of a POI.…”

Analysis of Mobile Technology Development Based on the Dual Perspectives of Tourism Managers and Customers

“…Everyone can use Wi-Fi everywhere and easy to access the trip advisors and tourism resources. In the context, many barriers which used to prevent or slow global travel are gradually falling [1].…”

“…This evolution helps and supports mobile users anywhere, anytime. Moreover, with the explosion of tourism on the WEB, it is not only provides the update information to managers, but also influences the decision of customers [1]. Therefore, this study intends to focus on the relationship between mobile technology and the tourism industry because people easily think about mobile electronics when the tourism is mentioned.…”