The author discusses using a Bayesian framework to choose between two restaurants based on reviews. Initially, with no reviews, all ratings are equally likely. The author then updates these beliefs based on observed data, using the Dirichlet distribution. The posterior ratings of the two restaurants are calculated, and the probability that restaurant A is better than restaurant B is determined. In the author’s case, there is an 85% probability that restaurant A is better.
Use Bayesian Framework to Choose the Best Restaurant
Are you struggling to choose between two restaurants based on their reviews? Let’s explore a practical AI solution called the Bayesian framework that can help you make a confident decision.
The Bayesian framework allows us to assume an initial distribution of ratings and update our beliefs based on observed data. Here’s how it works:
Set Initial Beliefs / Prior
Before any reviews, we know nothing about the probabilities of each rating. So, we start with a uniform distribution where all ratings are equally likely. This gives us an average rating of 3, which is the most probable.
To estimate the prior probabilities, we can sample from the uniform distribution using the Dirichlet distribution. This helps us determine the prior ratings’ means based on the sampled probabilities.
Update Beliefs
To update our initial beliefs, we multiply the prior beliefs by the likelihood of observing the data. The observed data is described by the Multinomial distribution.
The Dirichlet distribution is a conjugate prior to the Multinomial likelihood, meaning our posterior distribution is also a Dirichlet distribution incorporating the observed data.
In our case, we have two restaurants: A and B. We have observed data in the form of reviews, and we can calculate the posterior ratings’ probabilities and means using the Dirichlet distribution.
Which Restaurant is Better?
The probability that one restaurant is better than the other depends on the average ratings. By comparing the posterior ratings’ means, we can determine the probability that restaurant A has a higher average rating than restaurant B.
Using this Bayesian approach, we can incorporate prior beliefs, which are particularly valuable when the number of reviews is small. However, when the number of reviews is large, the initial beliefs have less impact on the posterior beliefs.
Evolve Your Company with AI
If you want to stay competitive and leverage AI to redefine your company’s way of work, consider the Bayesian framework for decision-making. Here are some practical tips for implementing AI solutions:
- Identify Automation Opportunities: Locate key customer interaction points that can benefit from AI.
- Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes.
- Select an AI Solution: Choose tools that align with your needs and provide customization.
- Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously.
If you need assistance with AI KPI management or want continuous insights into leveraging AI, you can connect with us at hello@itinai.com. Explore our AI Sales Bot at itinai.com/aisalesbot, designed to automate customer engagement and manage interactions across all customer journey stages.
List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- A Bayesian Way of Choosing a Restaurant
- Towards Data Science – Medium
- Twitter – @itinaicom
