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Advisor(s)
Abstract(s)
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: (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.
Description
ECMS 2017- 31st European Conference on Modelling and Simulation - May 23rd - May 26th, 2017
Budapest, Hungary
Keywords
Collaborative Filtering Personalisation Prediction Models Multi-criteria Ratings Tourism Crowd-Sourcing Recommender Systems Data Analytics