Recommendation rules to personalize itineraries for tourists in an unfamiliar city

Abstract

Currently, tourism has become increasingly related to new technologies. In this context, itinerary planning and trip recommendation are challenging tasks for tourists with different profiles. Tourists are generally not familiar with different Points-of-Interest (POIs) in a new city. To this end, they need to select and organize POIs that align with their interest preferences and trip constraints such as departure location, available trip duration, etc. as an itinerary. In this paper, we propose to extract recommendation rules using Multi-Objective Evolutionary Algorithms (MOEAs), to find a trade-off between two objectives: (1) maximizing the POIs popularity, and (2) maximizing the time user interest. We conducted a comparative study of several Multi-Objective Evolutionary Algorithms (MOEAs), such as NSGAII, SPEA2, and IBEA, based on the Flickr dataset of different cities. Our findings confirm the efficiency of NSGA-II in generating recommendation rules to personalize itineraries for tourists in unfamiliar cities.