nextrec 0.4.2

A comprehensive recommendation library with match, ranking, and multi-task learning models

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A Unified, Efficient, and Scalable Recommendation System Framework

Introduction

NextRec is a modern recommendation framework built on PyTorch, delivering a unified experience for modeling, training, and evaluation. It follows a modular design with rich model implementations, data-processing utilities, and engineering-ready training components. NextRec focuses on large-scale industrial recall scenarios on Spark clusters, training on massive offline parquet features.

Why NextRec

• Unified feature engineering & data pipeline: Dense/Sparse/Sequence feature definitions, persistent DataProcessor, and batch-optimized RecDataLoader, matching offline feature training/inference in industrial big-data settings.
• Multi-scenario coverage: Ranking (CTR/CVR), retrieval, multi-task learning, and more marketing/rec models, with a continuously expanding model zoo.
• Developer-friendly experience: Stream processing/training/inference for csv/parquet/pathlike data, plus GPU/MPS acceleration and visualization support.
• Efficient training & evaluation: Standardized engine with optimizers, LR schedulers, early stopping, checkpoints, and detailed logging out of the box.

Architecture

NextRec adopts a modular and low-coupling engineering design, enabling full-pipeline reusability and scalability across data processing → model construction → training & evaluation → inference & deployment. Its core components include: a Feature-Spec-driven Embedding architecture, the BaseModel abstraction, a set of independent reusable Layers, a unified DataLoader for both training and inference, and a ready-to-use Model Zoo.

The project borrows ideas from excellent open-source rec libraries. Early layers referenced torch-rechub but have been replaced with in-house implementations. torch-rechub remains mature in architecture and models; the author contributed a bit there—feel free to check it out.

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Installation

You can quickly install the latest NextRec via pip install nextrec; Python 3.10+ is required.

Tutorials

See tutorials/ for examples covering ranking, retrieval, multi-task learning, and data processing:

• movielen_ranking_deepfm.py — DeepFM training on MovieLens 100k
• example_ranking_din.py — DIN training on the e-commerce dataset
• example_multitask.py — ESMM multi-task training on the e-commerce dataset
• movielen_match_dssm.py — DSSM retrieval on MovieLens 100k

To dive deeper, Jupyter notebooks are available:

• Hands on the NextRec framework
• Using the data processor for preprocessing

Current version [0.4.2]: the matching module is not fully polished yet and may have compatibility issues or unexpected errors. Please raise an issue if you run into problems.

5-Minute Quick Start

We provide a detailed quick start and paired datasets to help you learn the framework. In datasets/ you’ll find an e-commerce sample dataset like this:

user_id	item_id	dense_0	dense_1	dense_2	dense_3	dense_4	dense_5	dense_6	dense_7	sparse_0	sparse_1	sparse_2	sparse_3	sparse_4	sparse_5	sparse_6	sparse_7	sparse_8	sparse_9	sequence_0	sequence_1	label
1	7817	0.14704075	0.31020382	0.77780896	0.944897	0.62315375	0.57124174	0.77009535	0.3211029	315	260	379	146	168	161	138	88	5	312	[170,175,97,338,105,353,272,546,175,545,463,128,0,0,0]	[368,414,820,405,548,63,327,0,0,0,0,0,0,0,0]	0
1	3579	0.77811223	0.80359334	0.5185201	0.91091245	0.043562356	0.82142705	0.8803686	0.33748195	149	229	442	6	167	252	25	402	7	168	[179,48,61,551,284,165,344,151,0,0,0,0,0,0,0]	[814,0,0,0,0,0,0,0,0,0,0,0,0,0,0]	1

Below is a short example showing how to train a DIN model. DIN (Deep Interest Network) won Best Paper at KDD 2018 for CTR prediction. You can also run python tutorials/example_ranking_din.py directly.

After training, detailed logs are available under nextrec_logs/din_tutorial.

import pandas as pd

from nextrec.models.ranking.din import DIN
from nextrec.basic.features import DenseFeature, SparseFeature, SequenceFeature

df = pd.read_csv('dataset/ranking_task.csv')

for col in df.columns and 'sequence' in col: # csv loads lists as text; convert them back to objects
    df[col] = df[col].apply(lambda x: eval(x) if isinstance(x, str) else x)

# Define feature columns
dense_features = [DenseFeature(name=f'dense_{i}', input_dim=1) for i in range(8)]

sparse_features = [SparseFeature(name='user_id', embedding_name='user_emb', vocab_size=int(df['user_id'].max() + 1), embedding_dim=32), SparseFeature(name='item_id', embedding_name='item_emb', vocab_size=int(df['item_id'].max() + 1), embedding_dim=32),]

sparse_features.extend([SparseFeature(name=f'sparse_{i}', embedding_name=f'sparse_{i}_emb', vocab_size=int(df[f'sparse_{i}'].max() + 1), embedding_dim=32) for i in range(10)])

sequence_features = [
    SequenceFeature(name='sequence_0', vocab_size=int(df['sequence_0'].apply(lambda x: max(x)).max() + 1), embedding_dim=32, padding_idx=0, embedding_name='item_emb'),
    SequenceFeature(name='sequence_1', vocab_size=int(df['sequence_1'].apply(lambda x: max(x)).max() + 1), embedding_dim=16, padding_idx=0, embedding_name='sparse_0_emb'),]

mlp_params = {
    "dims": [256, 128, 64],
    "activation": "relu",
    "dropout": 0.3,
}

model = DIN(
    dense_features=dense_features,
    sparse_features=sparse_features,
    sequence_features=sequence_features,
    mlp_params=mlp_params,
    attention_hidden_units=[80, 40],
    attention_activation='sigmoid',
    attention_use_softmax=True,
    target=['label'],                                     # target variable
    device='mps',                                         
    embedding_l1_reg=1e-6,
    embedding_l2_reg=1e-5,
    dense_l1_reg=1e-5,
    dense_l2_reg=1e-4,
    session_id="din_tutorial",                            # experiment id for logs
)

# Compile model with optimizer and loss
model.compile(
            optimizer = "adam",
            optimizer_params = {"lr": 1e-3, "weight_decay": 1e-5},
            loss = "focal",
            loss_params={"gamma": 2.0, "alpha": 0.25},
        )

model.fit(
    train_data=df,
    metrics=['auc', 'gauc', 'logloss'],  # metrics to track
    epochs=3,
    batch_size=512,
    shuffle=True,
    user_id_column='user_id'             # used for GAUC
)

# Evaluate after training
metrics = model.evaluate(
    df,
    metrics=['auc', 'gauc', 'logloss'],
    batch_size=512,
    user_id_column='user_id'
)

Platform Compatibility

The current version is 0.4.2. All models and test code have been validated on the following platforms. If you encounter compatibility issues, please report them in the issue tracker with your system version:

Platform	Configuration
MacOS latest	MacBook Pro M4 Pro 24GB RAM
Ubuntu latest	AutoDL 4070D Dual GPU
CentOS 7	Intel Xeon 5138Y 96 cores 377GB RAM

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Supported Models

Ranking Models

Model	Paper	Year	Status
FM	Factorization Machines	ICDM 2010	Supported
AFM	Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks	IJCAI 2017	Supported
DeepFM	DeepFM: A Factorization-Machine based Neural Network for CTR Prediction	IJCAI 2017	Supported
Wide&Deep	Wide & Deep Learning for Recommender Systems	DLRS 2016	Supported
xDeepFM	xDeepFM: Combining Explicit and Implicit Feature Interactions	KDD 2018	Supported
FiBiNET	FiBiNET: Combining Feature Importance and Bilinear Feature Interaction for CTR Prediction	RecSys 2019	Supported
PNN	Product-based Neural Networks for User Response Prediction	ICDM 2016	Supported
AutoInt	AutoInt: Automatic Feature Interaction Learning	CIKM 2019	Supported
DCN	Deep & Cross Network for Ad Click Predictions	ADKDD 2017	Supported
DCN v2	DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems	KDD 2021	In Progress
DIN	Deep Interest Network for CTR Prediction	KDD 2018	Supported
DIEN	Deep Interest Evolution Network	AAAI 2019	Supported
MaskNet	MaskNet: Feature-wise Gating Blocks for High-dimensional Sparse Recommendation Data	2020	Supported

Retrieval Models

Model	Paper	Year	Status
DSSM	Learning Deep Structured Semantic Models	CIKM 2013	Supported
DSSM v2	DSSM with pairwise BPR-style optimization	-	Supported
YouTube DNN	Deep Neural Networks for YouTube Recommendations	RecSys 2016	Supported
MIND	Multi-Interest Network with Dynamic Routing	CIKM 2019	Supported
SDM	Sequential Deep Matching Model	-	Supported

Multi-task Models

Model	Paper	Year	Status
MMOE	Modeling Task Relationships in Multi-task Learning	KDD 2018	Supported
PLE	Progressive Layered Extraction	RecSys 2020	Supported
ESMM	Entire Space Multi-task Model	SIGIR 2018	Supported
ShareBottom	Multitask Learning	-	Supported
POSO	POSO: Personalized Cold-start Modules for Large-scale Recommender Systems	2021	Supported
POSO-IFLYTEK	POSO with PLE-style gating for sequential marketing tasks	-	Supported

Generative Models

Model	Paper	Year	Status
TIGER	Recommender Systems with Generative Retrieval	NeurIPS 2023	In Progress
HSTU	Hierarchical Sequential Transduction Units	-	In Progress

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Contributing

We welcome contributions of any form!

How to Contribute

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add AmazingFeature')
4. Push your branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Before submitting a PR, please run tests using pytest test/ -v or python -m pytest to ensure everything passes.

Code Style

• Follow PEP8
• Provide unit tests for new functionality
• Update documentation accordingly

Reporting Issues

When submitting issues on GitHub, please include:

• Description of the problem
• Reproduction steps
• Expected behavior
• Actual behavior
• Environment info (Python version, PyTorch version, etc.)

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License

This project is licensed under the Apache 2.0 License.

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Contact

• GitHub Issues: Submit an issue
• Email: [e-mail]

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Acknowledgements

NextRec is inspired by the following great open-source projects:

• torch-rechub — Flexible, easy-to-extend recommendation framework
• FuxiCTR — Configurable, tunable, and reproducible CTR library
• RecBole — Unified, comprehensive, and efficient recommendation library

Special thanks to all open-source contributors!

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