Reinforcement Learning for Personalized Product Recommendation

Project Overview

This project integrates reinforcement learning (RL) into personalized product recommendation systems, focusing on a Monte Carlo RL model that considers both user preferences and budget constraints. The goal is to recommend products by optimizing for both customer satisfaction and budget compliance, providing a dynamic, personalized shopping experience.

Key Highlights:

• Monte Carlo RL: A model-free reinforcement learning approach, utilizing episodes to simulate and evaluate user interactions with products.
• Budget Constraints: Recommendations are optimized based on user-defined budget limits, ensuring product suggestions are both personalized and financially feasible.
• Reward Signals: The model's performance is analyzed under various reward signal schemes to understand how reward parameters affect convergence and recommendation quality.

👨‍💻 The Stack

Techniques Used

Monte Carlo Methods:

• Episode-Based Learning: The agent interacts with the environment, gathering data over complete episodes, and revises its value estimates based on the observed returns.
• Value Estimation: For a state or state-action pair, the value is estimated by averaging the returns collected from multiple episodes.
• First-Visit & Every-Visit Methods: Two approaches for value estimation. First-Visit updates the value on the agent's first visit to a state within an episode, while Every-Visit updates the value every time the state is visited.
• Exploration vs Exploitation: The agent balances exploration (trying new actions) and exploitation (selecting the best-known actions based on value estimates).

Process Flow

1. Data Preparation:

   • Organize and structure the input data (user preferences, product features, and budget) required for the RL model.

2. Initialization:

   • Set initial state values (V) for each action (product). These initial estimates serve as the starting point for the learning process.

3. Episode Generation:

   • Simulate episodes in which the agent interacts with the environment (product catalog) and selects products based on user preferences and budget constraints.

4. Terminal Reward Calculation:

   • After each episode, calculate a terminal reward based on whether the selected products' total cost falls within the specified budget.

5. Value Update:

   • Update the value estimates for each action based on the terminal rewards and observed state-action pairs during the episode.

How to Run

1. Install Dependencies: Make sure you have Python installed, then run:

   pip install -r requirements.txt

2. Run the Model: Execute the Monte Carlo RL script to train the model and evaluate recommendations:

   python src/monte_carlo_rl.py

3. Analyze Results: Check the results/ directory for performance metrics and insights on the recommendation quality.

CODE EXECUTION

The Code implements a Monte Carlo model for decision making with epsilon-greedy exploration.

Imports:

• pandas for data manipulation
• numpy for numerical computations
• matplotlib.pyplot for plotting
• warnings to suppress warnings

Function Definition:

def MCModelv1(data, alpha, e, epsilon, budget, reward):
  # ... function body ...

Function Arguments:

• data: Pandas DataFrame containing product information
• alpha: Learning rate for updating V values
• e: Number of episodes (simulations)
• epsilon: Exploration rate for epsilon-greedy selection
• budget: Budget constraint for selecting products
• reward: List of rewards for each product (currently not used)

Function Body:

1. Define States:

   • Identify unique ingredients (products) from the data.

2. Initialize V_0:

   • Set the initial value (V_0) for each product in the data. V_0 represents the expected value of an action (product).

3. Iterate Over Episodes:

   • Loop for the specified number of episodes (e).

4. Episode Run:

   • For each episode:
     • Implement epsilon-greedy selection:
       • If it's the first episode (random selection): Choose a random product for each ingredient.
       • Otherwise:
         • With probability epsilon, randomly choose a product for each ingredient.
         • With probability (1 - epsilon), choose the product with the highest V value for each ingredient.

5. Calculate Terminal Reward:

   • Check if the selected products fit within the budget.
     • If yes, set the terminal reward to 1.
     • If no, set the terminal reward to -1.

6. Update V Values:

   • Update the V value for each product based on the selected product, the terminal reward, and the learning rate (alpha).

7. Output:

   • Return various outputs including:
     • Sum of V for all actions at each episode
     • Sum of V for the cheapest and expensive actions
     • Optimal actions selected based on V values
     • Actions chosen in each episode

Model Execution:

• Set hyperparameters (alpha, epsilon, budget, etc.).
• Call the MCModelv1 function with the data and hyperparameters.
• The function returns various outputs for analysis.

Plotting and Analysis:

• The code uses matplotlib to plot various aspects of the model's behavior, such as the sum of V values over episodes and the optimal actions chosen.

Interactive Visualization (Commented Out):

• The code includes commented-out sections for creating interactive visualizations using plotly.
• These sections demonstrate how to create animations to explore the impact of different hyperparameters on model performance.

Overall, this code implements a Monte Carlo model for decision making with epsilon-greedy exploration. You can adjust the hyperparameters and analyze the outputs to understand how the model behaves and choose the best actions in your specific scenario.

Future Work

• Advanced RL Algorithms: Explore more sophisticated reinforcement learning techniques like Q-learning or deep RL for enhanced precision.
• Real-World Data: Apply the model to real-world datasets to further validate its effectiveness and refine its performance.

Contributing

Feel free to open issues or submit pull requests if you'd like to contribute to this project.

License

This project is licensed under the MIT License. See the LICENSE file for details.

This `README.md` provides a clear overview of the project, the techniques used, the process, and instructions on how to run the model. Let me know if you'd like to add or adjust anything!