# RePlay: a Recommendation Framework for Experimentation and Production Use

arXiv: https://arxiv.org/abs/2409.07272 · HTML: https://mlsanity.com/paper/2409.07272/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 27 references that are in the map.
- First posted on arXiv 11 Sep 2024.

## Builds on (most cited first, 18 of 18)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018)
- [RecBole 2.0: Towards a More Up-to-Date Recommendation Library](https://mlsanity.com/paper/2206.07351/) (2022)
- [Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling](https://mlsanity.com/paper/1810.12027/) (2018)
- [Turning Dross Into Gold Loss: is BERT4Rec really better than SASRec?](https://mlsanity.com/paper/2309.07602/) (2023)
- [Metadata Embeddings for User and Item Cold-start Recommendations](https://mlsanity.com/paper/1507.08439/) (2015)
- [gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling](https://mlsanity.com/paper/2308.07192/) (2023)
- [Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation](https://mlsanity.com/paper/2103.02590/) (2021)
- [A General Offline Reinforcement Learning Framework for Interactive Recommendation](https://mlsanity.com/paper/2310.00678/) (2023)
- [Quality Metrics in Recommender Systems: Do We Calculate Metrics Consistently?](https://mlsanity.com/paper/2206.12858/) (2022)
- [Does It Look Sequential? An Analysis of Datasets for Evaluation of Sequential Recommendations](https://mlsanity.com/paper/2408.12008/) (2024)
- [User Retention-oriented Recommendation with Decision Transformer](https://mlsanity.com/paper/2303.06347/) (2023)
- [From Variability to Stability: Advancing RecSys Benchmarking Practices](https://mlsanity.com/paper/2402.09766/) (2024)
- [Microsoft Recommenders: Tools to Accelerate Developing Recommender Systems](https://mlsanity.com/paper/2008.13528/) (2020)
- [Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions](https://mlsanity.com/paper/2307.14906/) (2023)

## Built on by (most cited first, 3 of 3)

- [Time to Split: Exploring Data Splitting Strategies for Offline Evaluation of Sequential Recommenders](https://mlsanity.com/paper/2507.16289/) (2025)
- [Sparse Autoencoders for Sequential Recommendation Models: Interpretation and Flexible Control](https://mlsanity.com/paper/2507.12202/) (2025)
- [Let It Go? Not Quite: Addressing Item Cold Start in Sequential Recommendations with Content-Based Initialization](https://mlsanity.com/paper/2507.19473/) (2025)

## Abstract (arXiv)

Using a single tool to build and compare recommender systems significantly reduces the time to market for new models. In addition, the comparison results when using such tools look more consistent. This is why many different tools and libraries for researchers in the field of recommendations have recently appeared. Unfortunately, most of these frameworks are aimed primarily at researchers and require modification for use in production due to the inability to work on large datasets or an inappropriate architecture. In this demo, we present our open-source toolkit RePlay - a framework containing an end-to-end pipeline for building recommender systems, which is ready for production use. RePlay also allows you to use a suitable stack for the pipeline on each stage: Pandas, Polars, or Spark. This allows the library to scale computations and deploy to a cluster. Thus, RePlay allows data scientists to easily move from research mode to production mode using the same interfaces.