Microsoft Recommenders: Tools to Accelerate Developing Recommender Systems
arXiv:2008.13528 · doi:10.1145/3298689.3346967
Abstract
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.
pages: 2; submitted to: RecSys '19
Cited by in corpus (5)
- A Re-visit of the Popularity Baseline in Recommender Systems
- Time to Split: Exploring Data Splitting Strategies for Offline Evaluation of Sequential Recommenders
- Multi-modal Embedding Fusion-based Recommender
- RePlay: a Recommendation Framework for Experimentation and Production Use
- Differentiable Fuzzy Neural Networks for Recommender Systems