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Title: GitHub - BaseModelAI/cleora: Cleora AI is a general-purpose open-source model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data. Created by Synerise.com team. · GitHub

Open Graph Title: GitHub - BaseModelAI/cleora: Cleora AI is a general-purpose open-source model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data. Created by Synerise.com team.

X Title: GitHub - BaseModelAI/cleora: Cleora AI is a general-purpose open-source model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data. Created by Synerise.com team.

Description: Cleora AI is a general-purpose open-source model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data. Created by Synerise.com team. - BaseModelAI/cleora

Open Graph Description: Cleora AI is a general-purpose open-source model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data. Created by Synerise.com team. - BaseMo...

X Description: Cleora AI is a general-purpose open-source model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data. Created by Synerise.com team. - BaseMo...

Mail addresses
cleora@synerise.com

Opengraph URL: https://github.com/BaseModelAI/cleora

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	https://github.com/BaseModelAI/cleora/blob/master/files/images/cleora.png
	https://github.com/BaseModelAI/cleora#the-graph-embedding-engine
Website	https://cleora.ai
Documentation	https://cleora.ai/docs
API Reference	https://cleora.ai/api
Benchmarks	https://cleora.ai/benchmarks
	https://github.com/BaseModelAI/cleora#achievements
SIGIR eCom Challenge 2020	https://sigir-ecom.github.io/ecom20DCPapers/SIGIR_eCom20_DC_paper_1.pdf
WSDM Booking.com Challenge 2021	http://ceur-ws.org/Vol-2855/challenge_short_3.pdf
Twitter Recsys Challenge 2021	https://recsys-twitter.com/competition_leaderboard/latest
KDD Cup 2021	https://ogb.stanford.edu/paper/kddcup2021/mag240m_SyneriseAI.pdf
	https://github.com/BaseModelAI/cleora#installation
	https://github.com/BaseModelAI/cleora#quick-start
	https://github.com/BaseModelAI/cleora#step-by-step-example
	https://github.com/BaseModelAI/cleora#cli
	https://github.com/BaseModelAI/cleora#key-advantages
	https://github.com/BaseModelAI/cleora#no-negative-sampling
	https://github.com/BaseModelAI/cleora#240x-faster-than-graphsage
	https://github.com/BaseModelAI/cleora#deterministic-embeddings
	https://github.com/BaseModelAI/cleora#heterogeneous-hypergraphs
	https://github.com/BaseModelAI/cleora#5-mb-zero-dependencies
	https://github.com/BaseModelAI/cleora#stable--inductive
	https://github.com/BaseModelAI/cleora#supported-algorithms
	https://github.com/BaseModelAI/cleora#advanced-embedding-modes
	https://github.com/BaseModelAI/cleora#batteries-included
full API reference	https://cleora.ai/api
	https://github.com/BaseModelAI/cleora#case-study-zomato
Read the full Zomato blog post →	https://www.zomato.com/blog/connecting-the-dots-strengthening-recommendations-for-our-customers-part-two/
	https://github.com/BaseModelAI/cleora#benchmarks
cleora.ai/benchmarks	https://cleora.ai/benchmarks
	https://github.com/BaseModelAI/cleora#classification-accuracy
	https://github.com/BaseModelAI/cleora#memory-efficiency
	https://github.com/BaseModelAI/cleora#scale-test-roadnet-ca-2-million-nodes
	https://github.com/BaseModelAI/cleora#library-comparison
	https://github.com/BaseModelAI/cleora#use-cases
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"Cleora: A Simple, Strong and Scalable Graph Embedding Scheme"	https://arxiv.org/abs/2102.02302
github.com/BaseModelAI/cleora	https://github.com/BaseModelAI/cleora
pypi.org/project/pycleora	https://pypi.org/project/pycleora/
	https://github.com/BaseModelAI/cleora#cite
our paper	https://arxiv.org/abs/2102.02302
	https://github.com/BaseModelAI/cleora#license
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	https://github.com/BaseModelAI/cleora#contributing
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