Trust-based Modelling of Multi-criteria Crowdsourced Data

Data Science and Engineering, Sep 2017

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.

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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)


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Fátima Leal, Benedita Malheiro, Horacio González-Vélez, Juan Carlos Burguillo. Trust-based Modelling of Multi-criteria Crowdsourced Data, Data Science and Engineering, 2017, pp. 1-11, DOI: 10.1007/s41019-017-0045-1