![]() | cberner-KNNImproved-r |
I implemented KNN with learning to find the best K, using cosine similarity and similarity weighting for prediction. Also the prediction is clamped to the range [1,5] since we know that ratings are in that range.
Existing runs on cberner-KNNImproved-r
1-10 of 10

ID | Program | Dataset | Tuned hyper. | User | Updated << | Status | Total time | Memory | Error |
---|---|---|---|---|---|---|---|---|---|
Run #40179 | cberner-KNNImproved-r | testlg | no | soryn1 | 2y253d ago | failed | 6s | 418M | |
Run #30316 | cberner-KNNImproved-r | CollaborativeFiltering-ZL | no | shakey | 4y139d ago | failed | 7m5s | 1523M | |
Run #17606 | cberner-KNNImproved-r | WordSegmentationData | no | internal | 5y290d ago | done | 0.340 | ||
Run #17607 | cberner-KNNImproved-r | CollaborativeFilteringData | no | internal | 5y290d ago | done | 1.71 | ||
Run #2068 | cberner-KNNImproved-r | cs281amovielens | no | internal | 7y195d ago | done | 1s | 415M | 0.761 |
Run #1336 | cberner-KNNImproved-r | movielens1m | no | jake | 7y195d ago | done | 1h29m | 833M | 0.905 |
Run #1312 | cberner-KNNImproved-r | eachmovie-1to5 | no | lester | 7y195d ago | failed | 24m27s | 2010M | |
Run #1231 | cberner-KNNImproved-r | eachmovie | no | lester | 7y195d ago | done | 1h28m | 1764M | 1.20 |
Run #875 | cberner-KNNImproved-r | movielens100k | no | cberner | 7y195d ago | done | 5m26s | 143M | 0.942 |
Run #874 | cberner-KNNImproved-r | collaborativefiltering-sample | no | cberner | 7y195d ago | done | 1s | 428M | 0.901 |
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Brozaz.com
Posted at 03/12/2018