Recommender-System Algorithms
Match users to items they're most likely to engage with.
Recommender systems match users to items across large catalogs, powering feeds, search, and personalization. The algorithms below progress from classic collaborative filtering through matrix factorization to the two-tower and ranking models used at production scale.
- Use matrix factorization or collaborative filtering for classic recommenders.
- Use two-tower neural networks plus ranking models for large-scale production systems.
| # | Algorithm | Best for | Common fields |
|---|---|---|---|
| 1 | Collaborative Filtering | User-item recommendations |
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| 2 | Matrix Factorization / SVD / ALS | Sparse rating or interaction data |
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| 3 | Content-Based Filtering | Recommending similar items |
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| 4 | Hybrid Recommenders | Combining user, item, and context signals |
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| 5 | Learning-to-Rank | Ranking search/recommendation results |
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| 6 | Deep Recommenders / Two-Tower Models | Large-scale retrieval and ranking |
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| 7 | Bandit Algorithms | Online recommendation optimization |
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