# Microsoft Recommenders: Tools to Accelerate Developing Recommender Systems

arXiv: https://arxiv.org/abs/2008.13528 · HTML: https://mlsanity.com/paper/2008.13528/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2020.

## Built on by (most cited first, 10 of 10)

- [A Comprehensive Survey of Evaluation Techniques for Recommendation Systems](https://mlsanity.com/paper/2312.16015/) (2023)
- [Time to Split: Exploring Data Splitting Strategies for Offline Evaluation of Sequential Recommenders](https://mlsanity.com/paper/2507.16289/) (2025)
- [CTRL-Rec: Controlling Recommender Systems With Natural Language](https://mlsanity.com/paper/2510.12742/) (2025)
- [Our Model Achieves Excellent Performance on MovieLens: What Does it Mean?](https://mlsanity.com/paper/2307.09985/) (2023)
- [RePlay: a Recommendation Framework for Experimentation and Production Use](https://mlsanity.com/paper/2409.07272/) (2024)
- [Calibrating the Predictions for Top-N Recommendations](https://mlsanity.com/paper/2408.11596/) (2024)
- [A Survey on LLM-based News Recommender Systems](https://mlsanity.com/paper/2502.09797/) (2025)
- [Differentiable Fuzzy Neural Networks for Recommender Systems](https://mlsanity.com/paper/2505.06000/) (2025)
- [Consistent Collaborative Filtering via Tensor Decomposition](https://mlsanity.com/paper/2201.11936/) (2022)
- [Visibility Allocation Systems: How Algorithmic Design Shapes Online Visibility and Societal Outcomes](https://mlsanity.com/paper/2510.17241/) (2025)

## Abstract (arXiv)

The purpose of this work is to highlight the content of the Microsoft Recommenders repository and show how it can be used to reduce the time involved in developing recommender systems. The open source repository provides python utilities to simplify common recommender-related data science work as well as example Jupyter notebooks that demonstrate use of the algorithms and tools under various environments.