recommendationfamily
BPR (Bayesian Personalized Ranking)
Pairwise ranking method for recommendation from implicit feedback, a standard baseline. Paper by Rendle et al.
Levelfamily
Classrecommendation
Parametersnot in the notes
Contextnot in the notes
Released9 May 2012
Opennessopen source
Runs onnot in the notes
Statusactive
Identity
- Developer
- not in the notes
- Family
- not in the notes
- Released
- 9 May 2012 sourcesannouncement page
- Weights published
- –
- Record checked
- 12 Oct 2026
Where this comes from
Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.
Architecture and Size
- Architecture
- other Pairwise ranking objective for implicit feedback
- Total parameters
- – sources
- Active parameters
- –
- Checkpoint parameters
- –
- Context length
- not in the notes
- Input
- table
- Output
- table
Where this comes from
Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).
Openness and Access
- Openness
- open source
- Licence
- MIT License sources
- Access routes
- not in the notes
Where this comes from
The licence file governs over registry tags and aggregator labels.
Deployment
- Runs on
- not in the notes
- Footprint
- arXiv date is the 2012 posting of the UAI 2009 paper. Licence is that of the RecBole implementation (MIT).
- Runtimes
- not in the notes
Where this comes from
Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.
Indonesia
- Indonesian support
- not stated
- Regional languages
- not in the notes
- Jakarta hosting
- unknown
- Languages
- not in the notes
Where this comes from
Indonesia fields are carried on every model; unknown is an allowed value.
Benchmark Results
The notes record no benchmark result for this model.
Lineage
The notes record no lineage for this model.
Industry Use Cases
No industry use case links this model.
Links
Developersnone recorded
ModelBPR (Bayesian Personalized Ranking)recommendation
Use CasesSuper-App Recommendation and PersonalisationRetail and LogisticsUse cases of models in the same R&D topic; none is recorded for this model itself.
Who Runs It
GrabIn production, 6 Oct 2026Deployments of these use cases; none names this model.
GrabIn production, 6 Oct 2026Deployments of these use cases; none names this model.Sources
- arxiv.org/abs/1205.2618 https://arxiv.org/abs/1205.2618
- github.com/RUCAIBox/RecBole https://github.com/RUCAIBox/RecBole
- github.com/RUCAIBox/RecBole2.0/blob/main/model_list.md https://github.com/RUCAIBox/RecBole2.0/blob/main/model_list.md
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "BPR (Bayesian Personalized Ranking)", https://aurion.semai.id/models/bpr/, records of October 2026.