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A Lot of Slots – Outliers Confinement in Review-Based Systems

  • Roberto Di Pietro
  • Marinella Petrocchi
  • Angelo Spognardi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8786)

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.

Keywords

Rating Aggregator Reputation System Breakdown Point Online Review Global Ranking 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Roberto Di Pietro
    • 1
    • 2
  • Marinella Petrocchi
    • 2
  • Angelo Spognardi
    • 2
  1. 1.Bell LaboratoriesParisFrance
  2. 2.IIT-CNRPisaItaly

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