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Adding RecBole. #65

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1 change: 1 addition & 0 deletions README.md
Original file line number Diff line number Diff line change
Expand Up @@ -60,6 +60,7 @@ The open-source recommender systems are:
1. [Gorse](https://gorse.io/) is an offline recommender system backend based on collaborative filtering written in Go. It implements mutiple rated or ranked based recommenders and multiple tools ranging from import/export tools to RESTful recommender server.
1. [Nvidia Merlin](https://developer.nvidia.com/nvidia-merlin) is an end-to-end recommender-on-GPU ecosystem composed by many tools, like [NVTabular](https://github.com/NVIDIA-Merlin/NVTabular) for fast *preprocessing / feature engineering* and [HugeCTR](https://github.com/NVIDIA-Merlin/HugeCTR) for high-throughput training and inference for large-scale *CTR prediction*. [Transformers4Rec](https://github.com/NVIDIA-Merlin/Transformers4Rec) is also part of Merlin ecosystem, providing TF an PyTorch APIs for *sequential and session-based recommendation* leveraging contextual features and NLP architectures from HuggingFace Transformers library.
1. [Alibaba EasyRec](https://github.com/alibaba/EasyRec) is a python recommender system that implements state of the art deep learning models used in common recommendation tasks: candidate generation(matching), scoring(ranking), and multi-task learning. It improves the efficiency of generating high performance models by simple configuration and hyper parameter tuning(HPO).
1. [RecBole](https://github.com/RUCAIBox/RecBole) is a python library for recommender systems with more than 100 recommendation algorithms implemented.

## Non-SaaS Product Recommender Systems

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