Leal, FátimaGonzález-Veléz, HoracioMalheiro, BeneditaBurguillo, Juan Carlos2017-08-282017-08-2820179780993244049http://hdl.handle.net/10400.22/10208ECMS 2017- 31st European Conference on Modelling and Simulation - May 23rd - May 26th, 2017 Budapest, HungaryBased on historical user information, collaborative filters predict for a given user the classification of unknown items, typically using a single criterion. However, a crowd typically rates tourism resources using multi-criteria, i.e., each user provides multiple ratings per item. In order to apply standard collaborative filtering, it is necessary to have a unique classification per user and item. This unique classification can be based on a single rating – single criterion (SC) profiling – or on the multiple ratings available – multicriteria (MC) profiling. Exploring both SC and MC profiling, this work proposes: (ı) the selection of the most representative crowd-sourced rating; and (ıı) the combination of the different user ratings per item, using the average of the non-null ratings or the personalised weighted average based on the user rating profile. Having employed matrix factorisation to predict unknown ratings, we argue that the personalised combination of multi-criteria item ratings improves the tourist profile and, consequently, the quality of the collaborative predictions. Thus, this paper contributes to a novel approach for guest profiling based on multi-criteria hotel ratings and to the prediction of hotel guest ratings based on the Alternating Least Squares algorithm. Our experiments with crowd-sourced Expedia and TripAdvisor data show that the proposed method improves the accuracy of the hotel rating predictions.engCollaborative FilteringPersonalisationPrediction ModelsMulti-criteria RatingsTourismCrowd-SourcingRecommender SystemsData Analyticsconference object2017-08-2110.7148/2017-057610.7148/2017-0576