Recommendation & Persona Matching Engine

A machine learning-based engine designed to cluster users into behavioral personas and deliver personalized recommendations based on dynamic match scoring. This solution improves engagement and optimizes content targeting.

Problem Statement

The system lacked a data-driven way to understand diverse user interests and behaviors, leading to generic, untargeted recommendations.

Solution

Leveraged clustering (K-Means) and vector similarity to segment users and compute match scores between user profiles and content, enabling dynamic personalization.

Architecture

• Data ingestion and transformation using Python and Pandas
• Feature extraction and engineering for user signals
• User clustering with K-Means (Scikit-learn)
• Cosine similarity-based match scoring algorithm
• Hosted and automated training on Azure ML Pipelines

Outcomes

• Higher engagement with recommended content
• Scalable pipeline for retraining on new user data
• Reusable persona definitions for marketing and product strategy

Technologies

Languages: Python, SQL
Libraries: Scikit-learn, Pandas, NumPy
Platform: Azure ML Studio