Usando Machine Learning para predecir rutas

What are Markov chains and how do they work?

Markov chains are a fascinating concept in probability theory that is used to predict transition probability between different states. The basic principle is that the next state of a process depends only on the current state, and not on the previous states. Thus, one can calculate the probabilities of moving from one page to another on a website or switching between different products in an e-commerce environment.

In the case discussed, imagine that we have three pages: Home, Products, and Contact. Based on how users navigate, we can create a predictive model with Markov chains. This model helps us understand the probabilities of a user moving from one page to another by assigning "weights" to the transitions.

For example:

• From Home page to Products, the probability may be 0.8.
• From Products back to Home, the probability might be 0.13.
• From Products to Contact, the probability could be 0.7.

These probabilities allow us to predict user behavior and optimize processes such as preloading necessary modules.

How do we visualize or represent Markov chains?

Markov chains can be represented both graphically and in matrices:

• Graphically: We use nodes and arrows, where each node represents a state (like a web page) and each arrow carries a weight representing the transition probability.
• Matrices: They represent probabilities of going from one state to another, being especially useful when dealing with more complex systems.

These representations facilitate the analysis and understanding of potential transitions between different states.

How does this apply to online trading?

The use of Markov chains in e-commerce has remarkable practical applications. For example, in an e-commerce site with multiple products and sections, user paths can be predicted. This allows the loading time of modules to be optimized by only pre-loading those that the predictive model considers most likely to be visited.

Once you have access to historical navigation data (e.g., from Google Analytics), you can feed it to a Markov chain-based model. This predictive model optimizes the user experience by pre-loading modules based on their expected behavior. Thus, a user who usually navigates from products to Home, will see his experience improved as the system anticipates his next step in an efficient way.

Practical tips

To work with predictive models based on Markov chains:

1. Collect historical data: You will need previous navigation data to create a robust model.
2. Generate the model: Use libraries and tools that allow you to create predictive models based on Markov chains.
3. Integrate and test: Implement the model in your web application and adjust according to the observed results.

This approach can significantly improve the loading efficiency and usability of web applications, adapting smoothly to the user's needs.