CARE Lite: Intelligent Product Recommender

Hackathon: Hack the Future: A Gen AI Sprint Powered by Data Team: Syncronyte

Overview

CARE Lite is a lightweight and deployable intelligent product recommender system designed to enhance personalization for niche e-commerce platforms. It addresses the challenges of manual recommendation processes by providing an automated and adaptive solution powered by a multi-agent system. CARE Lite combines the speed and predictability of rule-based logic with the continuous learning capabilities of a lightweight machine learning model to deliver relevant product suggestions, ultimately aiming to boost customer engagement and drive sales.

Problem Statement

In the competitive e-commerce landscape, personalized product recommendations are crucial for improving customer engagement and increasing conversion rates. Many niche e-commerce platforms struggle with:

• Labor-intensive manual data collection and segmentation.
• Inefficient recommendation generation based on rudimentary segmentation.
• Limited ability to capture nuanced customer preferences.
• Delayed and suboptimal product recommendations leading to lower conversion rates.

Existing open-source recommendation engines can often be too complex and resource-intensive for smaller platforms. CARE Lite offers a lightweight and immediately deployable alternative.

Proposed Solution

CARE Lite utilizes a multi-agent system to automate and personalize product recommendations:

• Data Extraction Agent (DEA): Automatically ingests and preprocesses customer Browse and purchase data from CSV datasets (customer_data_collection.csv and product_recommendation_data.csv).
• Rule-Based Recommendation Agent (RRA): Applies dynamic, manually curated rules to generate initial recommendations (e.g., recommending top-rated items from frequently viewed categories).
• Feedback Integration Agent (FIA): Employs a lightweight logistic regression model to incorporate real-time user interaction feedback (clicks, dwell time) and dynamically adjust recommendation thresholds.
• Human Override: A merchant dashboard (conceptualized) allows for real-time monitoring and manual adjustments when needed.

This hybrid approach combines the immediate effectiveness of rule-based recommendations with the continuous improvement offered by adaptive feedback learning.

Key Features

• Automated Data Processing: Simplifies data ingestion and preparation.
• Dynamic Rule Engine: Enables flexible and easily modifiable recommendation rules.
• Adaptive Learning: Continuously improves recommendation accuracy based on user interactions.
• Lightweight and Deployable: Optimized for small-to-medium e-commerce platforms with minimal infrastructure requirements.
• Scalability Roadmap: Designed with future migration to cloud databases in mind.
• Potential for Generative AI Integration: Future plans include using generative AI to suggest dynamic rule modifications.

Technologies Used

• Core Programming Language: Python 3.x
• Web Framework: Flask
• Data Processing & Storage: Pandas, SQLite
• AI/ML Components: Logistic regression (scikit-learn), Generative AI (conceptual)
• Version Control: Git

Getting Started

Follow these steps to set up and run CARE Lite:

Prerequisites

• Python 3.x installed on your system.
• pip package installer.

Installation

1. Clone the repository:

   git clone [Your GitHub Repository Link]
   cd CARE_Lite

2. Install the required Python packages:

   pip install -r requirements.txt

   (Note: You might need to create a requirements.txt file with the necessary dependencies: Flask, pandas, scikit-learn)

Dataset Setup

1. Place your customer_data_collection.csv and product_recommendation_data.csv files in the data/ directory.
2. Crucially, within the data/customer_data_collection.csv file or in a configuration setting, specify whether the data is a real-world sample or synthetic. This information is important for understanding the context of the recommendations.

Running the Application

1. Navigate to the project root directory.
2. Run the Flask application:

   python main.py

   (Note: Ensure main.py contains the Flask application setup.)

The API should now be running (likely on http://127.0.0.1:5000/ by default). Refer to the main.py file for available API endpoints.

Code Structure

CARE_Lite/ ├── main.py # Flask API entry point ├── agents/ │ ├── init.py │ ├── data_extraction.py # DataExtractionAgent │ ├── rule_recommender.py # RuleBasedRecommendationAgent │ └── feedback_agent.py # FeedbackIntegrationAgent ├── database/ │ ├── init.py │ ├── db_interface.py # Manages SQLite interactions │ └── init_db.py # Initializes the database ├── data/ │ ├── customer_data_collection.csv # Customer dataset (specify if real or synthetic) │ └── product_recommendation_data.csv # Product dataset └── utils/ └── helpers.py # Utility functions

Usage

The main.py file likely exposes a RESTful API. You can interact with it to get product recommendations. Example (conceptual):

Send a GET request to an endpoint like /recommendations/<user_id> to retrieve personalized product recommendations for a specific user. The API will utilize the agents to process data and generate the recommendations based on the defined logic.

(Note: The specific API endpoints and request/response formats will depend on the implementation in main.py.)

Future Enhancements

• Implement the Merchant Dashboard for real-time monitoring and manual rule adjustments.
• Migrate from SQLite to cloud-hosted databases (e.g., PostgreSQL, NoSQL) for improved scalability and performance.
• Expand the agent framework to handle more complex recommendation scenarios.
• Integrate advanced generative AI techniques via prompt engineering to suggest dynamic rule modifications based on evolving user trends.
• Extend CARE Lite to cover multi-channel customer interactions.
• Develop more sophisticated machine learning models for the Feedback Integration Agent.

Contributing

Contributions to CARE Lite are welcome! If you have ideas for improvements, bug fixes, or new features, please feel free to:

1. Fork the repository.
2. Create a new branch for your feature or fix.
3. Make your changes and commit them.
4. Push your changes to your fork.
5. Submit a pull request to the main repository.

Please ensure your contributions align with the project's goals and coding standards.