Microsoft Recommenders: Tools to Accelerate Developing Recommender Systems
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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.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Aug 2020.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 2; 2025: 5.
10 papers.
- 2023A Comprehensive Survey of Evaluation Techniques for Recommendation Systemscited by 12
- 2025Time to Split: Exploring Data Splitting Strategies for Offline Evaluation of Sequential Recommenderscited by 12
- 2025CTRL-Rec: Controlling Recommender Systems With Natural Languagecited by 4
- 2023Our Model Achieves Excellent Performance on MovieLens: What Does it Mean?cited by 3
- 2024RePlay: a Recommendation Framework for Experimentation and Production Usecited by 3
- 2024Calibrating the Predictions for Top-N Recommendationscited by 2
- 2025A Survey on LLM-based News Recommender Systemscited by 1
- 2025Differentiable Fuzzy Neural Networks for Recommender Systemscited by 1
- 2022Consistent Collaborative Filtering via Tensor Decompositioncited by 0
- 2025Visibility Allocation Systems: How Algorithmic Design Shapes Online Visibility and Societal Outcomescited by 0
- 2025Visibility Allocation Systems: How Algorithmic Design Shapes Online Visibility and Societal Outcomes20 Oct 2025
- 2025CTRL-Rec: Controlling Recommender Systems With Natural Language14 Oct 2025
- 2025Time to Split: Exploring Data Splitting Strategies for Offline Evaluation of Sequential Recommenders22 Jul 2025
- 2025Differentiable Fuzzy Neural Networks for Recommender Systems9 May 2025
- 2025A Survey on LLM-based News Recommender Systems13 Feb 2025
- 2024RePlay: a Recommendation Framework for Experimentation and Production Use11 Sep 2024
- 2024Calibrating the Predictions for Top-N Recommendations21 Aug 2024
- 2023A Comprehensive Survey of Evaluation Techniques for Recommendation Systems26 Dec 2023
- 2023Our Model Achieves Excellent Performance on MovieLens: What Does it Mean?19 Jul 2023
- 2022Consistent Collaborative Filtering via Tensor Decomposition28 Jan 2022