Search, retrieval & recommendation· 2007
Probabilistic Matrix Factorization
On the reading lists
- #358 in the Top 500 (2026 edition)
- #25 in Search, retrieval & recommendation (2026 edition)
Why it matters
Established scalable probabilistic latent-factor models for sparse, large-scale rating prediction.
Condensed from the expert lists' notes.
The record
- First released
- 2007 (from OpenAlex)
- Citations
- 4,943 citations, as counted by
Semantic Scholar (non-commercial licence) - Named by
- both expert lists
- Score
- 0.405 · computed rank 358 · consensus 0.33, must-read 0, citations percentile 0.875 (2000s · retrieval), awards 0, existing lists 0. Method
Data sources
Catalogue data from OpenAlex (CC0), Crossref (CC0 metadata; no abstracts are shown), arXiv, and Papers With Code (CC BY-SA 4.0), and
Semantic Scholar (used under its non-commercial API licence). Each count names its source. See the method.