recommendationfamily
DCN (Deep and Cross Network)
Network that learns bounded-degree feature crosses for ad click prediction. Paper by Wang et al. (2017).
Levelfamily
Classrecommendation
Parametersnot in the notes
Contextnot in the notes
Released17 Aug 2017
Opennessopen source
Runs onnot in the notes
Statusactive
Lineage
successor
Where this comes from
Parent and child relations from model cards, Hugging Face base_model fields and the genealogy tables of the research notes. The tree shows two generations each way, 2 models. Confidence and evidence are the notes' own.
Identity
- Developer
- Google, Stanford University
- Family
- not in the notes
- Released
- 17 Aug 2017 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 Explicit feature crossing layers plus deep network
- 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
- Licence is that of the RecBole implementation (MIT). AURION also holds dlrm-dcnv2, the MLPerf training workload.
- 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.
Industry Use Cases
No industry use case links this model.
Links
ModelDCN (Deep and Cross Network)Google, recommendation
DCN (Deep and Cross Network)Google, recommendationUse 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/1708.05123 https://arxiv.org/abs/1708.05123
- 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
- arxiv.org/html/1708.05123 https://arxiv.org/html/1708.05123
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "DCN (Deep and Cross Network)", https://aurion.semai.id/models/dcn/, records of October 2026.