Abstract
Review-based websites such as, e.g., Amazon, eBay, TripAdvisor, and Booking have gained an extraordinary popularity, with millions of users daily consulting online reviews to choose the best services and products fitting their needs. Some of the most popular review-based websites rank products by sorting them aggregating the single ratings through their arithmetic mean. In contrast, recent studies have proved that the median is a more robust aggregator, in terms of ad hoc injections of outlier ratings. In this paper, we focus on four different types of ratings aggregators. We propose to the slotted mean and the slotted median, and we compare their mathematical properties with the mean and the median. The results of our experiments highlight advantages and drawbacks of relying on each of these quality indexes. Our experiments have been carried out on a large data set of hotel reviews collected from Booking.com, while our proposed solutions are rooted on sound statistical theory. The results shown in this paper, other than being interesting on their own, also call for further investigations.
This research has been partially funded by the Tuscany region project MyChoice and by the Registro.it project MIB (My Information Bubble).
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Di Pietro, R., Petrocchi, M., Spognardi, A. (2014). A Lot of Slots – Outliers Confinement in Review-Based Systems. In: Benatallah, B., Bestavros, A., Manolopoulos, Y., Vakali, A., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2014. WISE 2014. Lecture Notes in Computer Science, vol 8786. Springer, Cham. https://doi.org/10.1007/978-3-319-11749-2_2
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DOI: https://doi.org/10.1007/978-3-319-11749-2_2
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