RePlay is an advanced framework designed to facilitate the development and evaluation of recommendation systems. It provides a robust set of tools covering the entire lifecycle of a recommendation system pipeline:

• Data Preprocessing and Splitting: Streamlines the data preparation process for recommendation systems, ensuring optimal data structure and format for efficient processing.
• Wide Range of Recommendation Models: Enables building of recommendation models from State-of-the-Art to commonly-used baselines and evaluate their performance and quality.
• Hyperparameter Optimization: Offers tools for fine-tuning model parameters to achieve the best possible performance, reducing the complexity of the optimization process.
• Comprehensive Evaluation Metrics: Incorporates a wide range of evaluation metrics to assess the accuracy and effectiveness of recommendation models.
• Model Ensemble and Hybridization: Supports combining predictions from multiple models and creating two-level (ensemble) models to enhance the quality of recommendations.
• Seamless Mode Transition: Facilitates easy transition from offline experimentation to online production environments, ensuring scalability and flexibility.

1. Diverse Hardware Support: Compatible with various hardware configurations including CPU, GPU, Multi-GPU.
2. Cluster Computing Integration: Integrating with PySpark for distributed computing, enabling scalability for large-scale recommendation systems.

• Quickstart
• Installation
• Resources
• Contributing to RePlay

pip install replay-rec[all]

Pyspark-based model and fast polars-based data preprocessing:

from polars import from_pandas
from rs_datasets import MovieLens

from replay.data import Dataset, FeatureHint, FeatureInfo, FeatureSchema, FeatureType
from replay.data.dataset_utils import DatasetLabelEncoder
from replay.metrics import HitRate, NDCG, Experiment
from replay.models import ItemKNN
from replay.utils.spark_utils import convert2spark
from replay.utils.session_handler import State
from replay.splitters import RatioSplitter

spark = State().session

ml_1m = MovieLens("1m")
K = 10

# convert data to polars
interactions = from_pandas(ml_1m.ratings)

# data splitting
splitter = RatioSplitter(
    test_size=0.3,
    divide_column="user_id",
    query_column="user_id",
    item_column="item_id",
    timestamp_column="timestamp",
    drop_cold_items=True,
    drop_cold_users=True,
)
train, test = splitter.split(interactions)

# datasets creation
feature_schema = FeatureSchema(
    [
        FeatureInfo(
            column="user_id",
            feature_type=FeatureType.CATEGORICAL,
            feature_hint=FeatureHint.QUERY_ID,
        ),
        FeatureInfo(
            column="item_id",
            feature_type=FeatureType.CATEGORICAL,
            feature_hint=FeatureHint.ITEM_ID,
        ),
        FeatureInfo(
            column="rating",
            feature_type=FeatureType.NUMERICAL,
            feature_hint=FeatureHint.RATING,
        ),
        FeatureInfo(
            column="timestamp",
            feature_type=FeatureType.NUMERICAL,
            feature_hint=FeatureHint.TIMESTAMP,
        ),
    ]
)

train_dataset = Dataset(feature_schema=feature_schema, interactions=train)
test_dataset = Dataset(feature_schema=feature_schema, interactions=test)

# data encoding
encoder = DatasetLabelEncoder()
train_dataset = encoder.fit_transform(train_dataset)
test_dataset = encoder.transform(test_dataset)

# convert datasets to spark
train_dataset.to_spark()
test_dataset.to_spark()

# model training
model = ItemKNN()
model.fit(train_dataset)

# model inference
encoded_recs = model.predict(
    dataset=train_dataset,
    k=K,
    queries=test_dataset.query_ids,
    filter_seen_items=True,
)

recs = encoder.query_and_item_id_encoder.inverse_transform(encoded_recs)

# model evaluation
metrics = Experiment(
    [NDCG(K), HitRate(K)],
    test,
    query_column="user_id",
    item_column="item_id",
    rating_column="rating",
)
metrics.add_result("ItemKNN", recs)
print(metrics.results)

Installation via pip package manager is recommended by default:

pip install replay-rec

In this case it will be installed the core package without PySpark and PyTorch dependencies. Also experimental submodule will not be installed.

To install experimental submodule please specify the version with rc0 suffix. For example:

pip install replay-rec==XX.YY.ZZrc0

In addition to the core package, several extras are also provided, including:

• [spark]: Install PySpark functionality
• [torch]: Install PyTorch and Lightning functionality
• [all]: [spark] [torch]

Example:

# Install core package with PySpark dependency
pip install replay-rec[spark]

# Install package with experimental submodule and PySpark dependency
pip install replay-rec[spark]==XX.YY.ZZrc0

To build RePlay from sources please use the instruction.

1. 01_replay_basics.ipynb - get started with RePlay.
2. 02_models_comparison.ipynb - reproducible models comparison on MovieLens-1M dataset.
3. 03_features_preprocessing_and_lightFM.ipynb - LightFM example with pyspark for feature preprocessing.
4. 04_splitters.ipynb - An example of using RePlay data splitters.
5. 05_feature_generators.ipynb - Feature generation with RePlay.
6. 06_item2item_recommendations.ipynb - Item to Item recommendations example.
7. 07_filters.ipynb - An example of using filters.
8. 08_recommending_for_categories.ipynb - An example of recommendation for product categories.
9. 09_sasrec_example.ipynb - An example of using transformer-based SASRec model to generate recommendations.
10. 10_bert4rec_example.ipynb - An example of using transformer-based BERT4Rec model to generate recommendations.
11. 11_sasrec_dataframes_comparison.ipynb - speed comparison of using different frameworks (pandas, polars, pyspark) for data processing during SASRec training.
12. 12_neural_ts_exp.ipynb - An example of using Neural Thompson Sampling bandit model (based on Wide&Deep architecture).
13. 13_personalized_bandit_comparison.ipynb - A comparison of context-free and contextual bandit models.
14. 14_hierarchical_recommender.ipynb - An example of using HierarchicalRecommender with user-disjoint LinUCB.

• Video guides:

  • Replay for offline recommendations, AI Journey 2021

• Research papers:

  • RePlay: a Recommendation Framework for Experimentation and Production Use Alexey Vasilev, Anna Volodkevich, Denis Kulandin, Tatiana Bysheva, Anton Klenitskiy. In The 18th ACM Conference on Recommender Systems (RecSys '24)
  • Turning Dross Into Gold Loss: is BERT4Rec really better than SASRec? Anton Klenitskiy, Alexey Vasilev. In The 17th ACM Conference on Recommender Systems (RecSys '23)
  • The Long Tail of Context: Does it Exist and Matter?. Konstantin Bauman, Alexey Vasilev, Alexander Tuzhilin. In Workshop on Context-Aware Recommender Systems (CARS) (RecSys '22)
  • Multiobjective Evaluation of Reinforcement Learning Based Recommender Systems. Alexey Grishanov, Anastasia Ianina, Konstantin Vorontsov. In The 16th ACM Conference on Recommender Systems (RecSys '22)
  • Quality Metrics in Recommender Systems: Do We Calculate Metrics Consistently? Yan-Martin Tamm, Rinchin Damdinov, Alexey Vasilev. In The 15th ACM Conference on Recommender Systems (RecSys '21)

We welcome community contributions. For details please check our contributing guidelines.