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SAR prediction for use with PySpark

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Simple Algorithm for Recommendation (SAR) is a neighborhood based algorithm for personalized recommendations based on user transaction history. SAR recommends items that are most similar to the ones that the user already has an existing affinity for. Two items are similar if the users that interacted with one item are also likely to have interacted with the other. A user has an affinity to an item if they have interacted with it in the past.

SARplus is an efficient implementation of this algorithm for Spark. More details can be found at sarplus@microsoft/recommenders.

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