Multi-criteria Ratings for Recommender Systems: An Empirical Analysis in the Tourism Domain

Fuchs, Matthias; Zanker, Markus

doi:10.1007/978-3-642-32273-0_9

Cited by 34 publications

(12 citation statements)

References 27 publications

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“…A lack of veracity is associated with TripAdvisor in some studies (Gerrard, 2012;Morrison, 2012; score (Fuchs & Zanker, 2012), is the second most underrated item in this study. The highest average of all items is location.…”

Section: Discussionsupporting

confidence: 47%

Does Verifying Uses Influence Rankings? Analyzing Booking.Com and Tripadvisor

Martín-Fuentes¹,

Mateu²,

Fernàndez³

2018

Tourism Analysis

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Electronic word of mouth (eWOM) is of recent and considerable importance in tourism, particularly because of the intangible nature of the industry. Users' online reviews are a source of information for other consumers, who take them into account before making a reservation at a lodging property. The aim of this study is to establish whether or not the anonymity of the reviews on TripAdvisor alters hotel rankings by comparing them with verified users' reviews on Booking.com. Moreover, the study analyzes whether or not the differences in the rating scales of both websites favor some hotels over others. A large amount of data is used in this study, with more than 40,000 hotels on Booking.com and 70,000 on TripAdvisor in 447 cities around the world, comparing the rankings of about 20,000 hotels matched on both websites. Our findings suggest that the behavior of both rankings is similar and the lack of veracity on TripAdvisor due to the anonymity in the user's verification system is baseless. In addition, some differences are found depending on the hotel category and region, due mainly to the unique rating scale on Booking.com (from 2.5 to 10) compared with the rating scale on TripAdvisor (from 1 to 5).

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Section: Discussionsupporting

confidence: 47%

Does Verifying Uses Influence Rankings? Analyzing Booking.Com and Tripadvisor

Martín-Fuentes¹,

Mateu²,

Fernàndez³

2018

Tourism Analysis

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“…Choosing between hotels: impact of bimodal rating summary… industry data from the tourism and hospitality domain crawled from TripAdvisor (Fuchs and Zanker 2012;Jannach et al 2014), and a public data set from Yelp 1 (for a general overview of these data sets, see Table 1).…”

Section: Attribute Selection and Experimental Designmentioning

confidence: 99%

Choosing between hotels: impact of bimodal rating summary statistics and maximizing behavioral tendency

Çoba

Rook

Zanker

2019

Inf Technol Tourism

Self Cite

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Rating summary statistics are basic aggregations that reflect users' assessments of experienced products and services in numerical form. Thus far, scholars primarily investigated textual reviews, but dedicated considerably less time and effort exploring the potential impact of plain rating summary statistics on people's choice behavior. Notwithstanding their fundamental nature, however, rating summary statistics also are relevant to electronic commerce in general, and to e-tourism in particular. In this work, we attempted to fill this void, by exploring the effects of different types of rating attributes (the mean rating value, the overall number of ratings, and the bimodality of rating distributions) on hotel choice behavior. We also investigated whether individual differences in the cause of people's maximizing behavioral tendency moderated the effect of rating summary statistics on hotel choice behavior. Results of an eye-tracked conjoint experiment show that people's high or low on decision difficulty as the cause of maximization determined whether and how rating summary statistics have an impact on the choice between hotels. Implications for the tourism and hospitality domain are addressed.

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“…Fuchs and Zanker [12] perform multi-criteria rating analysis based on a TripAdvisor dataset. First, they use multiple linear regression (MLR) to identify correlations, patterns and trends among the TripAdvisor dataset parameters.…”

Section: Related Workmentioning

confidence: 99%

Trust-based Modelling of Multi-criteria Crowdsourced Data

et al. 2017

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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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Multi-criteria Ratings for Recommender Systems: An Empirical Analysis in the Tourism Domain

Cited by 34 publications

References 27 publications

Does Verifying Uses Influence Rankings? Analyzing Booking.Com and Tripadvisor

Does Verifying Uses Influence Rankings? Analyzing Booking.Com and Tripadvisor

Choosing between hotels: impact of bimodal rating summary statistics and maximizing behavioral tendency

Trust-based Modelling of Multi-criteria Crowdsourced Data

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