SessionM’s robust recommendation engine provides scoring at scale for large data sets, using machine learning to turn historical purchase data into powerful customer insights.
Using matrix factorization techniques, SessionM’s recommendation engine generates personalized scores on a per-customer and per-product basis, without requiring extensive knowledge on either customer demographics or product attributes.
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