A Personalized Tourist Trip Design Algorithm for Mobile Tourist Guides

Abstract

& Mobile tourist guides evolve towards automated personalized tour planning devices. The contribution of this article is to put forward a combined artificial intelligence and metaheuristic approach to solve tourist trip design problems (TTDP). The approach enables fast decision support for tourists on small footprint mobile devices. The orienteering problem, which originates in the operational research literature, is used as a starting point for modelling the TTDP. The problem involves a set of possible locations having a score and the objective is to maximize the total score of the visited locations, while keeping the total time (or distance) below the available time budget. The score of a location represents the interest of a tourist in that location. Scores are calculated using the vector space model, which is a well-known technique from the field of information retrieval. The TTDP is solved using a guided local search metaheuristic.In order to compare the performance of this approach with an algorithm that appeared in the literature, both are applied to a real data set from the city of Ghent. A collection of tourist points of interest with descriptions was indexed and subsequently queried with popular interests, which resulted in a test set of TTDPs. The approach presented in this article turns out to be faster and produces solutions of better quality.

The impact of user- and system-initiated personalization on the user experience at large sports events

“…However, the benefits of personalization have been shown in a number of other scenarios -e.g. tourist guides (Souffriau et al, 2008), office applications (Bergman et al, 2004), e-commerce (Georgiadis, 2005), and learning systems (Economides, 2009). Although personalization (in its broadest sense) undoubtedly provides benefits, the evidence is not wholly unequivocal.…”

Personalized Trip Recommendation with POI Availability and Uncertain Traveling Time

“…Finally, we consider the uncertain traveling time between POIs through the completion probability constraint, which is absent in OP. While most works on OP focus on heuristic approaches [23,24] to estimate the global optimum of OP, we present an optimal solution to trip recommendation through a prefix based depth-first search strategy with a focus on efficiency through incremental reconstruction and dominance based pruning of routes.…”

Personalized Tourist Route Generation

“…The requirements of the problem a PET has to solve can be modeled as the Tourist Trip Design Problem (TTDP) [10,11]. The Orienteering Problem (OP) [12] is the most basic version of the TTDP.…”