Comments (4)
This makes sense. I have changed it on GitHub. Please install the package from there for now. It will be part of the next scheduled release.
from recommenderlab.
I'm currently looking at the code in predict.R and trying to figure out what line 23 and 25 are for. I think that removing these lines will solve the problem: the ratings in "rm" are already normalized and consist of (I think?) the model's predictions. No need to do any further evaluations using the newdata variable, right? The matrix rm is already the desired output as far as I can judge
from recommenderlab.
That is correct. I am currently thinking about what the right thing to do is here. Maybe I should not replace the predictions with the known values.
from recommenderlab.
Thank you for looking into this. I think removing the lines I mentioned should do the trick, and as you said: replacing the predictions with known values does not make sense, as they are predictions and not ground truths :). It would be much appreciated if this can be changed in the code soon, as I am highly dependent of the software for my current research.
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Related Issues (20)
- Efficient way to read a large rating csv file to realratingmatrix object HOT 1
- UBCF returns 1 as rating for predicted values with the weighted flag on binaryRatingMatrix HOT 2
- Regression from version 0.2-5 to 0.2-6 in Recommender.predict behavior HOT 4
- User-based Collaborative Filtering fails in nearest neighbor assignment HOT 1
- MovieLens metadata HOT 3
- Confusion about Confusion Matrix HOT 7
- Is "calcPredictionAccuracy" working correctly? HOT 5
- No negative cosine similarities for IBCF with user mean-centered ratings HOT 4
- Memory efficiency of RECOM_RANDOM (and, possibly, RECOM_POPULAR) HOT 7
- Extensions for sampling "known"/"unknown" recommendations in test set HOT 3
- Problem with `keepModel` option in `evaluationScheme.evaluate()` method HOT 3
- `summary()` methods for more classes in recommenderlab HOT 1
- `@Dim` method for `binaryRatingMatrix` class appears to be inverted HOT 2
- Implementation of eALS HOT 2
- Recommenderlab with Predict error HOT 4
- Evaluation Scheme doesn't work for Large Real Rating Matrix HOT 3
- Implicit ALS Bug
- rmse() function missing HOT 1
- interestMeasure: parameter method is now deprecated in AR recommender
- BIN_AR method throws an error "Unknown interest measure to sort by."
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