TripAdvisor is a business service that works as a reputation system to guarantee quality in tourism experience. This kind of new service is based on Big Data technologies and characterized by generating, managing and summarizing, even with rating indexes, a quantitative experimental size of information, representing a frontier issue for data analysis. These data are organized and offered to users by a filter system aimed at recommending consumer’s choices. Through a methodological design oriented to reward competitive quality, this acts as a crowdsourced evaluation system. In this paper, we suppose that information provided through the website can be biased because past reviews and ratings can affect the process of data production. On the basis of an empirical research for approximately 26.000 scores on TripAdvi- sor multipoint scale organized into 8-years time series and harvested by R software, we observe non-linear dynamics and skewed distribution among values of the scale. In our study, we observed that the main goal of crowd rating platforms is to exten- sively rank subsets of a population of units. This is achieved through the systematic employment of estimation techniques of evaluative measures. We propose a design of rating indexes that reflects the original missions of crowd rating: to pragmatically decrease the risk of a bad experience for the customer, to coherently benchmark, and to reliably rank a list of competing units.
|Titolo:||A Preference Index Design for Big Data|
TOMASELLI, Venera (Primo) (Corresponding)
|Data di pubblicazione:||2021|
|Appare nelle tipologie:||2.1 Contributo in volume (Capitolo o Saggio)|