Candidate Generation: Two-Tower Models and Collaborative Filtering
~45 min read
How to narrow millions of items to hundreds of candidates in milliseconds: two-tower retrieval, matrix factorization, and content-based approaches.
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Key points
- •Two-tower models pre-compute item embeddings offline and serve only user embeddings online — this is what makes sub-10ms retrieval over millions of items possible
- •The candidate generation stage must optimize for recall, not precision — any great item not retrieved here can never be recommended regardless of ranker quality
- •Collaborative filtering captures community taste (users like you liked X) but fails cold start; content-based works for new items but causes over-specialization — production systems blend both