Cleora is used in AI projects for the biggest and most innovative companies
200x faster than DeepWalk,
4x-8x faster than Pytorch-BigGraph by Facebook
Cleora computes embeddings of your relational data. Entities such as clients, products, stores, accounts, and others can be represented with embeddings, just like Word2Vec or BERT for text or CLIP for images. Cleora embeddings are behavioral - they represent entities by their behavior history, which has the form of large graphs.
What can you build with Cleora Embeddings?
- Recommender Systems
- Client Segmentation
- Propensity Prediction
- Lifetime Value Modeling
- Churn Prediction
- and many other types of enterprise models
Key technical features of Cleora embeddings
The embeddings produced by Cleora are different from those produced by Node2vec, Word2vec, DeepWalk or other systems in this class by a number of key properties:
Efficiency
Cleora is two orders of magnitude faster than Node2Vec or DeepWalk. We’ve embedded graphs with 100s of billions of edges on a single machine without GPUs. It likely is the fastest approach possible.
Inductivity
As Cleora embeddings of an entity are defined only by interactions with other entities, vectors for new entities can be computed on-the-fly.
Cross-dataset compositionality
Thanks to stability of Cleora embeddings, embeddings of the same entity on multiple datasets can be combined by averaging, yielding meaningful vectors.
Stability
All starting vectors for entities are deterministic, which means that Cleora embeddings on similar datasets will end up being similar. Methods like Word2vec, Node2vec or DeepWalk return different results with every run.
Extreme parallelism and performance
Cleora is written in Rust utilizing thread-level parallelism for all calculations except input file loading. In practice this means that the embedding process is often faster than loading the input data.
Dim-wise independence
Thanks to the process producing Cleora embeddings, every dimension is independent of others. This property allows for efficient and low-parameter method for combining multi-view embeddings with Conv1d layers.
We used Cleora for customer-restaurants graph data in the National Capital Region (NCR) area. And to our delight, the embedding generation was superfast (i.e <5 minutes). For context, do remember that GraphSAGE took ~20hours for the same data in the NCR region.
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