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Recommender systems are growing progressively more popular in online retail because of their ability to offer personalized experiences to unique users. Mean Average Precision at K ([email protected]) is typically the metric of choice for evaluating the performance of a recommender systems. However, the use of additional diagnostic metrics and visualizations can offer deeper and sometimes surprising insights into a model's performance. This article explores Mean Average Recall at K (
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