mlsanityData to 7 Oct 2026

RePlay: a Recommendation Framework for Experimentation and Production Use

arXiv 2409.07272 ↗First posted 11 Sep 2024cs.IR cs.LG cs.SEcited by 3

Not on the map. Why?

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 18 of its 27 references that are in the map. First posted on arXiv 11 Sep 2024.

Builds on

18 of 27 references.

References · 18

  1. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
  2. 2018Self-Attentive Sequential Recommendationcited by 1,169
  3. 2017Neural Collaborative Filteringcited by 1,108
  4. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 952
  5. 2018Variational Autoencoders for Collaborative Filteringcited by 308
  6. 2022RecBole 2.0: Towards a More Up-to-Date Recommendation Librarycited by 39
  7. 2018Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modelingcited by 31
  8. 2023Turning Dross Into Gold Loss: is BERT4Rec really better than SASRec?cited by 30
  9. 2015Metadata Embeddings for User and Item Cold-start Recommendationscited by 29
  10. 2023gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Samplingcited by 29
  11. 2021Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluationcited by 28
  12. 2023A General Offline Reinforcement Learning Framework for Interactive Recommendationcited by 18
6 more
  1. 2022Quality Metrics in Recommender Systems: Do We Calculate Metrics Consistently?cited by 17
  2. 2024Does It Look Sequential? An Analysis of Datasets for Evaluation of Sequential Recommendationscited by 15
  3. 2023User Retention-oriented Recommendation with Decision Transformercited by 14
  4. 2024From Variability to Stability: Advancing RecSys Benchmarking Practicescited by 12
  5. 2020Microsoft Recommenders: Tools to Accelerate Developing Recommender Systemscited by 10
  6. 2023Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functionscited by 9

Built on by

Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025Time to Split: Exploring Data Splitting Strategies for Offline Evaluation of Sequential Recommenderscited by 12
  2. 2025Sparse Autoencoders for Sequential Recommendation Models: Interpretation and Flexible Controlcited by 2
  3. 2025Let It Go? Not Quite: Addressing Item Cold Start in Sequential Recommendations with Content-Based Initializationcited by 1