Abstract:
Intelligent tourism can increase the interest of nomadic tourists in discovering new cities. However, many Points of Interest
(POIs) are available, creating an information overload for tourists when choosing POIs to visit. For this reason, CARS (Context-Aware
Recommendation Systems) can play an important role by exploiting the experiences of previous tourists and their contexts to recommend
attractive POIs. Consequently, choosing the right POI recommendation algorithm (RA) for CARS is crucial because it involves the costly
intervention of real tourists during the test phase. In order to make this phase more cost-effective, we can test several RAs simultaneously
in order to assess their limitations in terms of cold start and tourist satisfaction. To compare these RAs, we propose in this article an
approach called SEPRA (Systematic Evaluation for POI Recommendation Algorithms), which allows us to carry out an initial online
evaluation of each tourist during their visit and a second offline evaluation of each CARS after the end of each POI path. To achieve
this objective, we designed and implemented a new smart tourism tool that makes POI recommendations using two algorithms: the first
is based on tourist/tourist similarity, and the second uses POI/POI similarity. These algorithms use memory-based collaborative filtering
and are executed in parallel by our tool in the form of CARSs, incorporating time or weather as context variables. To evaluate these
systems during their test phases, Our approach enables: (1) the calculation of prediction accuracy; (2) the examination of the relevance
of the recommended POIs; and (3) the estimation of the acceptance rate of the recommendation process. Finally, the experimental results
obtained with our approach show that the algorithm based on tourist similarity is more resistant to the cold start problem during the test
phase and has a better satisfaction rate than the algorithm based on POI similarity.
