Prepare for cross validation-based benchmarking #60
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In this case, use `length(pred)` as a recommendation size.
Codecov Report
@@ Coverage Diff @@
## master #60 +/- ##
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+ Coverage 80.14% 80.24% +0.09%
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Files 26 26
Lines 801 815 +14
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+ Hits 642 654 +12
- Misses 159 161 +2
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`ealuate()` unnecessarily made predictions for all user-item pairs. Comparison must be done between truth vs. pred.
Returned list of item-score tuples from `recommend` is already sorted by the scores.
when a recommender is evaluated by a ranking metric.
`truth` must be a ranked list of observed items for correct evaluation.
Cross validation has some randomness, and it may or may not return very poor/good result.
Adjust cross validation test cases to increase the probability of seeing an empty `truth` list.
If `n` equals to the number of all samples, `n`-fold CV is same as LOOCV.
Top-k recommendation for every single user is costly. It'd be recommended to parallelize whenever possible.
takuti
changed the title
by checking the size of test samples
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Review and tweak
cross_validationandevaluatefor #26