Cleora 2.0 builds on its predecessor's success, offering fresh features and optimizations that enable even broader usage across diverse, relational data sets.
What is Cleora?
Cleora is simply a high-performance framework designed to make stable, inductive representations of entities within heterogeneous data structures. By leveraging graph-based principles and optimized algorithms, Cleora allows data professionals to uncover hidden patterns and relationships, making it an invaluable tool for device learning and data discipline applications.
The tool has earned global designation in prestigious data discipline competitions, including:
- 1st place at SIGIR eCom Challenge 2020
- 2nd place and Best Paper Award at WSDM Booking.com Challenge 2021
- 2nd place at Twitter RecSys Challenge 2021
- 3rd place at KDD Cup 2021
These accomplishments item Cleora's versatility and its ability to deliver exceptional results in advice systems, graph analytics, and natural language processing.
Cleora is now available as a Python package pycleora. Key improvements compared to the erstwhile version:
- Performance optimizations: ~10x faster embedding times
- Performance optimizations: importantly reduced memory usage
- Latest research: improved embedding quality
- New feature: can make graphs from Python iterators in addition to tsv files
- New feature: seamless integration with NumPy
- New feature: item attributes support via customized embedding initialization
- New feature: adjustable vector projection/normalization after each propagation step
Why Choose Cleora?
Unlike conventional graph embedding techniques, Cleora operates straight on relational data without requiring explicit graph construction. This not only reduces computational overhead but besides eliminates the request for external dependencies. Key advantages include:
- Speed: Cleora can make embeddings for millions of nodes in a substance of minutes, thanks to its optimized algorithms.
- Simplicity: The tool is straightforward to implement, with minimal setup and configuration.
- Inductive Capabilities: Cleora supports the induction of embeddings for new, unseen entities, making it perfect for dynamic, real-world applications.
- Versatility: Its usage cases scope from recommender systems to fraud detection, NLP, and client behaviour analysis.
Key usability features of Cleora embeddings
The method properties described above imply good production-readiness of Cleora, which from the end-user position can be summarized as follows:
- heterogeneous relational tables can be embedded without any artificial data pre-processing
- mixed interaction + text datasets can be embedded with ease
- cold start problem for fresh entities is non-existent
- real-time updates of the embeddings do not require any separate solutions
- multi-view embeddings work out of the box
- temporal, incremental embeddings are unchangeable out of the box, with no request for re-alignment, rotations, or another methods
- extremely large datasets are supported and can be embedded within seconds/minutes
Open-Source Commitment
Synerise remains committed to fostering innovation through open-source contributions. We value the feedback and support of the global data discipline community, which has played a crucial function in the evolution of Cleora.
To learn more, access Cleora.ai 2.0, or contribute to its development, visit our authoritative GitHub repository.
Join us in redefining what's possible in entity representation learning with Cleora.ai 2.0.
