nexREC - AI Graph-Based Recommendation Engine
An AI recommendation prototype that uses graph-based relationships to produce more relevant suggestions than simple popularity or category matching.
The Challenge
Many recommendation systems stay shallow. They suggest what is popular, what is recent, or what sits in the same category, but they miss deeper relationships between users, items, preferences, and behavior.
Our Solution
We explored a graph-based recommendation model that represents users, items, and interactions as connected data. This makes it possible to reason over relationships and generate suggestions that reflect patterns beyond simple filtering.
The Outcome
The prototype demonstrates how graph intelligence can improve discovery for e-commerce, media, learning platforms, and any product where personalization affects engagement.
Our Approach
Modeled users, items, and interactions as a relationship graph.
Explored similarity, neighborhood, and preference signals within the graph.
Designed the system so multiple recommendation strategies can be compared.
Focused on explainability, so recommendations can be traced back to meaningful relationships.
Technology Stack
Business Impact
Graph-based recommendations can help platforms increase engagement, improve product discovery, and create more personal user experiences across commerce, content, and education.
Use AI to make discovery smarter, more personal, and more useful.
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