Stochastic error and biases remain in blind wine ratings

Simply Predicting Wine Review Ratings

Symmetric Error Structure in Stochastic DEA

Predicting wine ratings using linear models

In this paper I analyze reviews taken from a popular wine review website. I use linear regression to tune a model suited for predicting wine ratings. I compare a two models and select the best one with the best parameters using k fold cross validation. The results indicate that better predictions come from using more of the features, rather than less of them. This suggests that both the age of ...

Food Recommendations: Biases that Underpin Ratings

The context in which preference information is gathered from users is recognised as one of the important factors in generating user models. Much research has been carried out into sensing and recording the physical and environmental contexts of user interactions, and it was shown that these affect the derived user modelling data. In this talk we introduce a new facet of context called a Reasoni...

symmetric error structure in stochastic dea