mouna99/dien
None observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 2917
- days_rel: n/a
- days_push: 2737
- n_releases_24m: 0
Adoption not part of the score
1211 stars · 411 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A TensorFlow implementation of the Deep Interest Evolution Network (DIEN) for click-through rate prediction, based on the Alibaba research paper. It also supports related CTR models such as DNN, PNN, Wide&Deep, and DIN, with prepared Amazon dataset scripts.
Use cases
- predict click-through rates for ads or recommendations
- reproduce the DIEN paper experiments
- model user interest evolution from behavior sequences
- compare CTR models like DIN, DIEN, and Wide&Deep
- train a CTR model on the Amazon dataset
When to choose
- you need a reference implementation of DIEN for CTR prediction research
- you want to benchmark sequence-based interest models against DIN and baselines
When to avoid
- you need a production-ready, maintained recommendation system
- you use TensorFlow 2.x or modern tooling
- you need a license permitting commercial use
Facets
library · maturity maintenance
machine-learning deep-learning machine-learning deep-learning python ctr-prediction recommendation tensorflow research-code attention-models algorithms
1 source
- readme: https://github.com/mouna99/dien · fetched 2026-08-28 · fd0822a5879f
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| mouna99/dien | main | 32 |
For agents
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem