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Profiling And Rating Prediction From Multi-Criteria Crowd-Sourced Hotel Ratings

Leal, Fátima, González-Vélez, Horacio, Malheiro, Benedita and Burguillo, Juan Carlos (2017) Profiling And Rating Prediction From Multi-Criteria Crowd-Sourced Hotel Ratings. In: Proceedings 31st European Conference on Modelling and Simulation. ECMS. ISBN 9780993244049

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Abstract

Based 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: ({) the selection of the most representative crowd-sourced rating; and ({{) 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.

Item Type: Book Section
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Tourism Industry
Divisions: School of Computing > Staff Research and Publications
Related URLs:
Depositing User: CAOIMHE NI MHAICIN
Date Deposited: 28 Aug 2017 10:05
Last Modified: 28 Aug 2017 10:05
URI: http://trap.ncirl.ie/id/eprint/2575

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