PaddlePaddle/PaddleRec
Recommendation Algorithm大规模推荐算法库，包含推荐系统经典及最新算法LR、Wide&Deep、DSSM、TDM、MIND、Word2Vec、Bert4Rec、DeepWalk、SSR、AITM，DSIN，SIGN，IPREC、GRU4Rec、Youtube_dnn、NCF、GNN、FM、FFM、DeepFM、DCN、DIN、DIEN、DLRM、MMOE、PLE、ESMM、ESCMM, MAML、xDeepFM、DeepFEFM、NFM、AFM、RALM、DMR、GateNet、NAML、DIFM、Deep Crossing、PNN、BST、AutoInt、FGCNN、FLEN、Fibinet、ListWise、DeepRec、ENSFM，TiSAS，AutoFIS等，包含经典推荐系统数据集criteo 、movielens等 observed · 2026-08-28
Health v2 · maintenance only
29/100
- Activity 14
- Release rhythm 8
- Longevity 100
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: 2318
- days_rel: n/a
- days_push: 518
- n_releases_24m: 0
Adoption not part of the score
4085 stars · 651 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
PaddleRec is a large-scale recommendation algorithm library built on PaddlePaddle, containing classic and state-of-the-art recommendation models such as LR, Wide&Deep, DeepFM, DIN, DIEN, MMOE, PLE, and many more. It also bundles standard recommendation datasets like Criteo and MovieLens to enable easy benchmarking and experimentation.
Use cases
- train a CTR prediction model like DeepFM or DIN
- build a multi-task recommendation model with MMOE or PLE
- benchmark recommendation algorithms on Criteo or MovieLens datasets
- implement sequential recommendation with GRU4Rec or BST
- deploy large-scale industrial recommendation models
- learn and prototype recommendation system algorithms
When to choose
- you want a comprehensive collection of ready-to-use recommendation algorithms in one library
- your team already uses the PaddlePaddle deep learning framework
- you need both classic and cutting-edge recommendation models with standard datasets included
- you are doing research or benchmarking on CTR prediction or multi-task learning for recommendations
When to avoid
- your stack is built on PyTorch or TensorFlow rather than PaddlePaddle
- you need a full production recommendation system platform rather than an algorithm library
- you only need simple collaborative filtering without deep learning models
- your team has no experience with PaddlePaddle and cannot invest in learning it
Facets
library · maturity active
machine-learning deep-learning data-science machine-learning deep-learning large-language-models python recommendation-systems collaborative-filtering ctr-prediction paddlepaddle deep-learning algorithms linux docker gpu
1 source
- readme: https://github.com/PaddlePaddle/PaddleRec · fetched 2026-08-28 · e6db595f1598
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| PaddlePaddle/PaddleRec | main | 29 |
For agents
markdown · JSON · MCP: product_card(name="PaddlePaddle/PaddleRec")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem