@rishabh835 wrote:
Hi All,
I just went through the recommender systems article and have a question about the evaluation metric ‘Recall’ and ‘Precision’. Recall is defined as TP/TP+FN.
Example: If a user likes 5 items and the recommendation engine decided to show 3 of them, then the recall will be 0.6.Let’s say I am using the collaborative filtering approach and have recommended movies then how do I know if whatever I have predicted is liked by the user or not?
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