Recommender Systems

Personalized recommendation engine builder. Six algorithm families + hybrid blending + evaluation suite. Always outputs three layers: technical model → evaluation metrics → marketer-ready insights + NBA.

How Recommendations Work — Plain Language

A recommender system answers one question: "What should this specific customer see next?"

Three approaches, in order of sophistication:

CONTENT-BASED:  "You liked X → show things similar to X"
                Uses: item descriptions, categories, attributes
                Good for: new items, cold-start users