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Say we have a recommender system in production which recommends 1 our of N items according to some internal algorithm f given inputs Xi for each user i, let's assume f is a black box model. We have historical data recording user's reaction yi (click, dismiss, conversion, could be delayed), to the recommendation.

How should I evaluate the performance of this recommender system offline given the data available?

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