Databasemovielens100k
100K MovieLens movie ratings dataset from http://www.grouplens.org/. 100,000 ratings (1-5) from 943 users on 1682 movies. Test set contains exactly 10 ratings per user. See included README.txt for more information.
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lester
1M
processed
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90570
9430
943
1682
1
5


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Existing runs on movielens100k 1-25 of 120 < > Action_refresh_blue
ID Program Dataset Tuned hyper. User Updated Status Total time Memory Error >>
Run #14836 self-reflective-multi-task-GP-with-side-info-r movielens100k no internal 3y46d ago done 1h20m 469M 0.923
Run #14841 self-reflective-multi-task-GP-r movielens100k no internal 3y46d ago done 1h20m 481M 0.928
Run #14792 localMTL movielens100k no internal 3y46d ago done 1h54m 424M 0.937
Run #1214 xterm-knnsvd_shrunk_unbias-r movielens100k no xterm 3y325d ago done 9m59s 425M 0.938
Run #1265 xterm-knnsvd_shrunk_unbias_rev-r movielens100k no xterm 3y325d ago done 8m28s 425M 0.938
Run #875 cberner-KNNImproved-r movielens100k no cberner 3y325d ago done 5m26s 143M 0.942
Run #14887 MyMediaLite-item-knn-pearson movielens100k no internal 3y46d ago done 7m37s 418M 0.946
Run #14877 MyMediaLite-user-knn-pearson movielens100k no internal 3y46d ago done 13m6s 418M 0.949
Run #1110 whoburg-ALS_K3_lambda2-r movielens100k no whoburg 3y325d ago done 37m54s 425M 0.950
Run #2106 mjanderson-matrix-factorization-r movielens100k no internal 3y325d ago done 2m6s 421M 0.954
Run #14857 MyMediaLite-biased-matrix-factorization-k-60 movielens100k no internal 3y46d ago done 8m31s 423M 0.954
Run #14892 MyMediaLite-item-knn-cosine movielens100k no internal 3y46d ago done 9m31s 423M 0.957
Run #1175 kenghao-knn_k50-r movielens100k no kenghaochang 3y325d ago done 10m0s 425M 0.959
Run #14882 MyMediaLite-user-knn-cosine movielens100k no internal 3y46d ago done 19m47s 418M 0.959
Run #1118 whoburg-ALS_K3-r movielens100k no whoburg 3y325d ago done 1h10m 60M 0.959
Run #1154 kenghao-matrix_em_k10-r movielens100k no kenghaochang 3y325d ago done 10m2s 424M 0.959
Run #1198 kenghao-matrix_em_k10_v2-r movielens100k no kenghaochang 3y325d ago done 4m19s 246M 0.959
Run #14742 MyMediaLite-matrix-factorization-k-5 movielens100k no internal 3y46d ago done 20s 417M 0.960
Run #13966 MML-BMF movielens100k no zenogantner 3y56d ago done 46s 423M 0.961
Run #13992 MyMediaLite-slopeone movielens100k no zenogantner 3y55d ago done 2m7s 423M 0.961
Run #1116 whoburg-ALS_K5-r movielens100k no whoburg 3y325d ago done 1h15m 425M 0.962
Run #14756 MyMediaLite-matrix-factorization-k-10 movielens100k no internal 3y46d ago done 23s 418M 0.965
Run #1039 whoburg-ALS_K1-r movielens100k no whoburg 3y325d ago done 4m6s 416M 0.968
Run #14897 MyMediaLite-user-item-baseline movielens100k no internal 3y46d ago done 9s 422M 0.972
Run #14872 MyMediaLite-biased-matrix-factorization-k-40 movielens100k no internal 3y46d ago done 6m29s 414M 0.973


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Comments:

  • Icon_profile
    Jacob Abernethy (jake)
    UC Berkeley Comp Sci
    Posted at 11/15/2009
    Netflix...
    Oriol - you should have joined Lester's netflix team. His team basically tied for first place, but didn't get the million because their submission was slightly later.
  • Icon_profile
    Oriol Vinyals (xterm)
    UC Berkeley
    Posted at 11/14/2009
    :)
    I did KNN plus SVD. I wanted to do FA but I'd rather use C++ to do that... R is too slow!
  • Icon_profile
    Jacob Abernethy (jake)
    UC Berkeley Comp Sci
    Posted at 11/14/2009
    Wow!
    xterm is in the lead!! Apparently K-nearest neighbor is the best thing to do with 100k ratings? That surprises me. We should ask the netflix master, Lester. Lester, what say you?
  • Icon_profile
    Jacob Abernethy (jake)
    UC Berkeley Comp Sci
    Posted at 11/13/2009
    Wow
    Y'all are still hammering away at the 100k dataset. I'm impressed. This is like a mini-netflix prize...

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