What is a hybrid recommender system?

A hybrid recommender system combines collaborative filtering, content-based filtering and often other methods, so each covers the others’ weak spots.

Collaborative filtering struggles with new items, and content-based filtering tends to repeat what someone already likes. A hybrid system combines them, for example by blending their scores, switching between them depending on how much data is available, or using one to fetch candidates and another to rank them.

Most large production systems are hybrids. Target’s stack, for example, layers embeddings, affinity models, next-item prediction and similarity models before ranking and re-ranking the results.