Trust-based Modelling of Multi-criteria Crowdsourced Data
Trust-based Modelling of Multi-criteria Crowdsourced Data
Fa´tima Leal 0 1 2 3 4 5
Benedita Malheiro 0 1 2 3 4 5
Juan Carlos Burguillo 0 1 2 3 4 5
Horacio Gonza´lez-Ve´lez 0 1 2 3 4 5
Juan Carlos Burguillo 0 1 2 3 4 5
0 EET/Uvigo - School of Telecommunications Engineering, University of Vigo , Vigo , Spain
1 Horacio Gonza ́lez-Ve ́lez
2 & Fa ́tima Leal
3 CCC/NCI - Cloud Competency Centre, National College of Ireland , Dublin , Ireland
4 ISEP/IPP - School of Engineering, Polytechnic Institute of Porto , Porto , Portugal
5 INESC TEC , Porto , Portugal
As a recommendation technique based on historical user information, collaborative filtering typically predicts the classification of items using a single criterion for a given user. However, many application domains can benefit from the analysis of multiple criteria, e.g. tourists usually rate attractions (hotels, attractions, restaurants, etc.) using multiple criteria. In this paper, we argue that the personalised combination of multi-criteria data together with the creation and application of trust models should not only refine the tourist profile, but also improve the quality of the collaborative recommendations. The main contributions of this work are: (1) a novel profiling approach which takes advantage of the multi-criteria crowdsourced data and builds pairwise trust models and (2) the k-NN prediction of user ratings using trust-based neighbour selection. Significant experimental work has been performed using crowdsourced datasets from the Expedia and TripAdvisor platforms.
Collaborative filtering Prediction models Multi-criteria ratings Tourism crowdsourcing Trust
1 Introduction
Coupled with information and communications
technologies, tourism crowdsourcing has significantly
revolutionised tourist behaviour over the past decade. Mobile
technologies provide tourists with permanent access to
endless web services which influence their decisions using
crowdsourced information. Such information is shared
collaboratively by tourists in well-known tourism
businessto-customer online platforms (e.g. TripAdvisor, Expedia
and Airbnb). They enable a tourist to actively share
mementos, comments, reviews and, most importantly, rate
their overall travel experience. By gathering voluntarily
shared feedback, these online platforms have essentially
become crowdsourcing platforms [
9
].
The value of crowdsourced tourism information is
crucial to businesses and clients alike. However, the voluntary
information sharing and the openness of crowdsourcing
systems raise reliability and integrity questions. Therefore,
when using crowdsourced data, it is necessary to take
trustworthiness into account in order to ensure the accuracy
and validity of the final results. Trust mechanisms must
arguably underpin crowdsourcing platforms in order to
validate both the quality level of the crowdsourced
information and indeed the users.
In this work, we have modelled tourists and tourism
attractions (‘resources’) employing multi-criteria tourism
information from crowdsourcing platforms coupled with
trust mechanisms in order to produce personalised
recommendations.
Personalised recommendations are often based on the
prediction of user classifications. Typically, the
crowdsourced classification of hotels involves multi-criteria
ratings, e.g. hotels are classified in the Expedia or
TripAdvisor platforms in terms of cleanliness, hotel
condition, service and staff, room comfort or overall opinion.
The personalised combination of multi-criteria
crowdsourced ratings together with trust modelling arguably
improves the tourist profile and, consequently, the accuracy
of the collaborative predictions.
Collaborative filtering is a classification-based
technique, i.e. depends on the classification each user gave to
the items he/she was exposed to [
5
]. Typically, this
classification corresponds to a unique rating. Whenever the
crowdsourced data hold multiple ratings per user and item,
first, it is necessary to decide which user classification to
use in order to apply collaborative filtering. This work
explores both profiling approaches: single criterion (SC)—
choosing the most representative of the crowdsourced user
ratings [
22, 23
]—and multi-criteria (MC)—combining the
different crowdsourced user ratings per item, using the
non-null rating average (NNRA) or the personalised
weighted rating average (PWRA), i.e. based on the
individual user rating profile.
This work proposes a new approach to provide online
tourism recommendations using collaborative filtering
via k-nearest neighbours (k-NN) algorithm. Additionally,
we apply Pearson correlation to determine the
correlation among users, and, then, build a decentralised trust
model depending on the selected recommendations
regarding the current data stream event. Therefore, this
research contributes to guest and hotel profiling—based
on multi-criteria ratings incorporating trust modelling—
and to the prediction of hotel guest ratings—bas (...truncated)