AI Recommendation System
A recommendation engine that learns from behavior and business data to surface what's actually relevant to each user.
Overview
The AI Recommendation System is an engineering project exploring how personalization can be built into a product without relying on generic, rule-based suggestion logic. It uses behavioral and contextual data — what a user viewed, purchased, searched or ignored — to generate ranked, relevant recommendations instead of static 'popular items' lists.
The problem
Most businesses either show the same generic suggestions to every user or rely on simple rule-based logic — 'customers who bought X also bought Y' — that doesn't adapt as behavior or catalog data changes. That leaves real personalization, and the engagement it drives, on the table.
Our approach
The system is built around a feature pipeline that captures user interactions and item attributes, feeding a model that scores and ranks candidate items per user context. Data collection, model scoring and the serving layer are kept separate, so recommendations can be recalculated as new behavior comes in without redesigning the product experience around them.
Key capabilities
What the system actually does.
Behavioral Signal Processing
Captures and structures user interaction data — views, purchases, searches — into usable model features.
Personalized Ranking
Scores and ranks items per user context instead of showing static, one-size-fits-all suggestions.
Cold-Start Handling
Falls back to contextual and catalog-based signals for new users or items with limited interaction history.
Pluggable Serving Layer
Sits behind an API so recommendations can be surfaced anywhere in a product experience.
How it works
System workflow.
Data Collection
Feature Engineering
Model Scoring & Ranking
API Serving
Feedback Loop
Data Collection
Feature Engineering
Model Scoring & Ranking
API Serving
Feedback Loop
Technology
Built with purpose-chosen tools.
System experience
From a product's point of view, recommendations arrive through a single API call and update as a user's behavior changes within the same session — no separate 'refresh' step, and no static list baked in at build time.
Why it matters
Recommendations built on real behavior rather than fixed rules keep pace with how a catalog and audience actually change. It's the difference between a suggestion shelf that feels generic and one that feels like it's paying attention.