Added r-precision

[daviddavo]

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 which has relevant items, we would ask the recommendation engine for the top recommendations, and see which of them are true positives.

Related Issues

• 2067

References

[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.

Checklist:

• [x] I have followed the contribution guidelines and code style for this project.
• [x] I have added tests covering my contributions.
• [x] I have updated the documentation accordingly.
• [x] I have signed the commits, e.g. git commit -s -m "your commit message".
• [x] This PR is being made to staging branch AND NOT TO main branch.

[daviddavo]

I haven't used Spark or PySpark before. I checked out the code, and it seems that in this case the function metrics are imported from PySpark, using RankingMetrics(), which doesn't implement r-precision, so I don't know where to start

[miguelgfierro]

got it, I just created an issue, let's merge, and we will implement the spark version in the future.