Sentence examples for most representative rating from inspiring English sources

First, the authors selected the overall as the most representative rating after determining the correlation between the multiple ratings, then applied data clustering (fuzzy c-means and k-means) to find the nearest neighbours and, finally, predicted the unknown hotel ratings using the Pearson correlation coefficient.

To be able to use multi-criteria ratings for profiling, we designed and experimented with two main approaches: (1) the identification of the most representative rating (MRR) with MLR and (2) the combination of the multi-criteria ratings into a single rating, per user and item, using NNRA and PWRA.

First, we apply a multiple linear regression (MLR) to identify the most representative rating (MRR).

Therefore, we analyse the most representative rating (MRR) using the Leal et al. approach [22] which relies on multiple linear regression.

They employed: (1) principal component analysis (PCA) for the selection of the most representative rating (dimensionality reduction); (2) expectation maximisation (EM) and adaptive neuro-fuzzy inference system (ANFIS) as prediction techniques; and (3) TripAdvisor data for evaluation.

Based on these results, we chose the overall rating as the most representative rating (MRR) of both HotelExpedia and TripAdvisor and, then, performed the overall rating prediction using k-NN algorithm.

The rating analysis comprised two different approaches: (1) the identification of the most representative hotel rating and (2) the combination of the multi-criteria guest ratings per hotel into a unique guest rating per hotel.

Additionally, the authors compare the available techniques using the most representative production indicators such as rate and efficiency.

However, our analyses have the advantage of having a larger cohort of people with diabetes (about 2,000 cases per year), along with a national probability sampling methodology, making it the most representative contemporary examination of death rates among the U.S. diabetic population.

The mean incidence rate between 1999 and 2002, the most representative part of the observation period, was 6.13 per 100,000 person years (95% CI 4.17 9.0).

Different working variables were studied in its development, including injection volume, flow rate, and fat amount as the most representative coextract.