Comments (5)
Hi. This is a good idea. I guess you want this to scale up the number of predictions you can make?
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yes
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You could save the Recommender object and then run several instances of R and load the Recommender object to create recommendations in parallel.
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That is an interesting idea. @mhahsler what is your general view on using recommenderlab in production for data larger than movielense and other example data sets?
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The code is not optimized for performance. Some recommendation algorithms will not scale well, others might...
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Related Issues (20)
- extract latent features from ALS HOT 1
- could I find the list of methords available Recommender(,methord=) HOT 1
- could I get the rank or score from the predict()? and reformat the result into datafram HOT 1
- 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
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