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R-precision can be defined as the precision@R, with R being the number of relevant documents for the query [1]. This means that for a certain user $u$ which has $R$ relevant items, we would ask the recommendation engine for the top $R$ recommendations, and see which of them are true positives.
[1]: C. D. Manning, P. Raghavan, and H. Schütze, ‘Chapter 8. Evaluation in information retrieval’, in Introduction to information retrieval, New York: Cambridge University Press, 2008, pp. 151–175.
Expected behavior with the suggested feature
Adding R-Precision will allow evaluating systems with some characteristics.
Other comments
Implementation idea:
Get the number of relevant documents (R) for all users
Get the top-k recommendations of the model where $k = \max R$
Calculate the R-precision for each user, and return the mean as in other metrics
The text was updated successfully, but these errors were encountered:
Description
R-precision can be defined as the precision@R, with R being the number of relevant documents for the query [1]. This means that for a certain user$u$ which has $R$ relevant items, we would ask the recommendation engine for the top $R$ recommendations, and see which of them are true positives.
[1]: C. D. Manning, P. Raghavan, and H. Schütze, ‘Chapter 8. Evaluation in information retrieval’, in Introduction to information retrieval, New York: Cambridge University Press, 2008, pp. 151–175.
Expected behavior with the suggested feature
Adding R-Precision will allow evaluating systems with some characteristics.
Other comments
Implementation idea:
The text was updated successfully, but these errors were encountered: