Crab - A Python Library for Recommendation Engines This library intends to be a reference for recommendation engines in Python Programming language. It is written in pure python to maximize the cross-platform issue and exposes the recommendation logic to your application by easy to use API REST via web services. The library is extensible, so the user can create new representations, algorithms and the design is optimized for performance. It is also open-source so everyone can use it. If you want to see our plan release/roadmap, please take a look at our Issues Tracker: http://github.com/marcelcaraciolo/crab/issues
This library intends to be a reference for recommendation engines in Python
Overview
A Python implementation of LightFM, a hybrid recommendation algorithm.
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Graph Neural Network based Social Recommendation Model. SIGIR2019.
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Cloud-based recommendation system
This project is based on cloud services to create data lake, ETL process, train and deploy learning model to implement a recommendation system.
The source code for "Global Context Enhanced Graph Neural Network for Session-based Recommendation".
GCE-GNN Code This is the source code for SIGIR 2020 Paper: Global Context Enhanced Graph Neural Networks for Session-based Recommendation. Requirement
This library intends to be a reference for recommendation engines in Python
Crab - A Python Library for Recommendation Engines
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