AI Recommendation Engines are powerful and common uses of AI. They are able to learn from all customer data and work out accurately what products particular customers may be interested in based on the behaviour and demographic of other customers. How a real AI Recommendation Engine/ Recommendation System (Recsys) works is different to how you might think - if you're used to traditional software techniques. It uses some sophisticated maths that are actually interesting, using three dimensional mappings as opposed to tonnes of database calls and bog standard code. Here I discuss how it all works.
We've been busy adding extra features to ELDR AI prior to release of v1.0 at the end of January. We have been able to successfully integrate dynamic Embeddings, Batch Normalisation and Dropouts in relevant layers throughout the AI Engine making it even more adaptable, robust and powerful.
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