Cleoraai

What is Cleora AI?

Cleora AI is a general-purpose open-source model designed for the efficient and scalable learning of stable and inductive entity embeddings specifically tailored for heterogeneous relational data. Developed by the Synerise.com team, Cleora AI provides a robust framework for handling complex data structures and generating high-quality embeddings that facilitate various machine learning tasks. Its innovative approach allows for quick adaptations to new entities and real-time updates, making it a versatile tool for data scientists and machine learning practitioners.

How to use Cleora AI?

1. Install Cleora: Begin by installing the Cleora Python package using the command `pip install pycleora`.
2. Prepare Your Data: Organize your data into a format that Cleora can process, typically a relational table where rows represent interactions between entities.
3. Create Hyperedges: Convert your data into hyperedges that represent relationships between entities, ensuring to group co-occurring entities appropriately.
4. Initialize Embeddings: Use the `SparseMatrix` class from the `pycleora` package to create a Markov transition matrix from your hyperedges.
5. Run Markov Propagation: Execute the Markov random walk algorithm to generate the embeddings, adjusting the number of walks as needed for your specific use case.
6. Normalize Embeddings: Normalize the resulting embeddings to ensure they reside on a hypersphere for better similarity comparisons.

What are the main features of Cleora AI?

• Open Source: Cleora AI is fully open-source, allowing for transparency and community contributions.
• Efficient Learning: It provides efficient learning of entity embeddings, significantly speeding up the embedding process compared to traditional methods.
• Scalable: Cleora AI can handle large datasets and complex relational data structures, making it suitable for enterprise-level applications.
• Inductive Embeddings: The model allows for the creation of embeddings for new entities on-the-fly, making it adaptable to dynamic datasets.
• Real-Time Updates: Cleora supports real-time updates of embeddings, facilitating the incorporation of new data without the need for retraining.

Who is Cleora AI for?

Cleora AI is aimed at data scientists, machine learning engineers, and researchers who work with heterogeneous relational data and require efficient methods for generating entity embeddings. It is particularly useful for organizations that deal with large datasets, such as e-commerce platforms, social networks, and academic research institutions, where understanding complex relationships between entities is critical for improving recommendations, predictions, and insights.

What are the use cases of Cleora AI?

1. Recommendation Systems: Cleora AI can be utilized to improve product recommendations by embedding user and product interactions.
2. Social Network Analysis: It can analyze relationships between users, helping to identify communities or influential nodes within social networks.
3. Fraud Detection: By embedding transaction data, Cleora can help identify patterns indicative of fraudulent activities in financial systems.