Dominika Basaj & Barbara Rychalska - Creating behavioral profiles of your customer

TL;DR

Sunrise presents CLIORA, a powerful and versatile graph embedding library for behavioral profiling and modeling with a focus on graphs, highlighting its universal applicability and success in competitions.

Transcript

we're from Sunrise we're a team of basically we're a huge company but inside this company we have a team of 10 people who are really heavily research focused we mostly focus on everything that is connected and linked with behalf behavioral profiling and modeling behavior of people with special focus on uh graphs because we feel that graphs do this ... Read More

Key Insights

• 😤 Sunrise is a team focused on behavioral profiling and graph modeling within a larger company.
• 📚 CLIORA is a powerful and universal graph embedding library that has been successful in competitions.
• 📈 Graph embeddings are a compact representation of node properties in a graph, allowing for efficient computation and analysis.
• 🍵 CLIORA is fast, scalable, and supports hypergraphs, making it suitable for handling large and diverse datasets.
• ❓ CLIORA's simple algorithm, combined with various optimizations, contributes to its competitive performance.
• ❓ CLIORA can be easily used in Python, despite being implemented in Rust.
• 👻 CLIORA allows for the combination of multiple embeddings and the ability to customize embeddings for different purposes.

Questions & Answers

Q: What is the purpose of graph embeddings?

Graph embeddings are used to represent the properties of nodes in a graph in a vector format, allowing for efficient computation and analysis. It condenses large adjacency matrices into shorter vectors while preserving information about node similarity.

Q: How does CLIORA compute graph embeddings?

CLIORA uses a simple algorithm based on matrix multiplication. It starts with a random embedding matrix and iteratively multiplies it with a Markov transition matrix, making each node's embedding more similar to those of its neighbors. Normalization is performed after each iteration to prevent embeddings from vanishing.

Q: How does CLIORA compare to other graph embedding systems?

CLIORA is fast and scalable, outperforming strong competitors for larger graphs. While some competitors may be more performant for smaller graphs, CLIORA consistently delivers competitive results. Its implementation in Rust, although surprising for a Python conference, can be easily used in Python.

Q: Can CLIORA handle large and diverse datasets?

Yes, CLIORA is designed to handle vast amounts of data with different flavors, such as purchase data, click data, and ratings. It supports hypergraphs and has the ability to chunk graphs for processing. Additionally, new nodes can be incorporated into an existing embedding by averaging their neighboring embeddings.

Key Insights:

• Sunrise is a team focused on behavioral profiling and graph modeling within a larger company.
• CLIORA is a powerful and universal graph embedding library that has been successful in competitions.
• Graph embeddings are a compact representation of node properties in a graph, allowing for efficient computation and analysis.
• CLIORA is fast, scalable, and supports hypergraphs, making it suitable for handling large and diverse datasets.
• CLIORA's simple algorithm, combined with various optimizations, contributes to its competitive performance.
• CLIORA can be easily used in Python, despite being implemented in Rust.
• CLIORA allows for the combination of multiple embeddings and the ability to customize embeddings for different purposes.
• Sunrise is developing a software-as-a-service version of CLIORA, called CLIORA Plus, with enhanced optimization and performance.

Summary & Key Takeaways

• Sunrise, a research-focused team within a larger company, specializes in behavioral profiling and modeling behavior, particularly with graphs.

• CLIORA is a graph embedding library designed by Sunrise for fast and scalable computation of graph embeddings, which represent the properties of nodes in a graph.

• CLIORA is efficient and simple to use, supporting CPU computation and hypergraphs, making it suitable for handling large datasets and complex structures.