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Advisor(s)
Abstract(s)
Tourism crowdsourcing platforms have a profound influence
on the tourist behaviour particularly in terms of travel planning. Not
only they hold the opinions shared by other tourists concerning tourism
resources, but, with the help of recommendation engines, are the pillar
of personalised resource recommendation. However, since prospective
tourists are unaware of the trustworthiness or reputation of crowd publishers,
they are in fact taking a leap of faith when then rely on the
crowd wisdom. In this paper, we argue that modelling publisher Trust &
Reputation improves the quality of the tourism recommendations supported
by crowdsourced information. Therefore, we present a tourism
recommendation system which integrates: (i) user profiling using the
multi-criteria ratings; (ii) k-Nearest Neighbours (k-NN) prediction of the
user ratings; (iii) Trust & Reputation modelling; and (iv) incremental
model update, i.e., providing near real-time recommendations. In terms
of contributions, this paper provides two different Trust & Reputation
approaches: (i) general reputation employing the pairwise trust values
using all users; and (ii) neighbour-based reputation employing the pairwise
trust values of the common neighbours. The proposed method was
experimented using crowdsourced datasets from Expedia and TripAdvisor
platforms.
Description
Keywords
Crowdsourcing Trust & Reputation Rating Prediction Tourism
Citation
Publisher
Springer International Publishing