Yum-Me

I chose Tipo Pasta Bar, an Italian restaurant in Singapore famous for its variety of pasta. It was particularly popular at that time as it allowed restaurant-goers to customize their pasta. 

It's not uncommon to find discrepancies in reviews on restaurant pages, but these mismatches in reviews can be misleading for consumers who rely on high star ratings (over 4 out of 5 stars) to judge a restaurant's quality.

In a fast-paced world with myriad options, time spent browsing through reviews to make a decision is costly and could potentially be a waste of time.

Additionally, detailed reviews are often wordy, and difficult to digest, and users may not have the time or patience to read through the entire review.

With so many opinions online, how do we differentiate what is credible or not?

How can users minimize effort (efficiency) in making a food decision that is accurate (effective) to their preferences? 

Through the onboarding survey, users will have to input data on the dependent variables (i.e., taste, texture, dietary preference, etc). Missing values will not be possible as the questions cannot be skipped or else the survey is considered incomplete. This is to ensure that the model is not skewed by any outlier or fluctuations in the data. 

This application will adopt a supervised machine-learning algorithm trained upon features with an output value to predict the target variable; which is the similarity in taste preferences between the primary user and other users. 

The model will use a recommendation system such as the Bayesian inference algorithm to predict the similarity between the user’s preferences using new data (e.g., if a user visits new restaurants) and prior knowledge of the user’s preferences (i.e., the survey responses).