MLcomp version alpha Statistics: 1213 users, 402 datasets, 328 programs, 9938 runs (0 queued, 0 running), 0 workers

eachmovie

EachMovie movie ratings dataset from HP/Compaq (more information on http://www.grouplens.org/). The original EachMovie dataset contained 2,811,983 ratings (1-6 stars) entered by 72,916 users for 1628 different movies. This sub-dataset includes the ratings of 30,000 randomly selected users with 20 or more ratings. A single rating from each user was withheld to form the test set. Ratings values outside of the 1-6 range have been discarded.

Format:	CollaborativeFiltering
User:	lester
Size:	23M
Status:	processed
Access:	restricted_access
#train	2079628
#test	29998
#rows	30000
#cols	0
minY
maxY

Run a program on this dataset

Select a program CollaborativeFiltering 1-25 of 107 < > Filter:

	Name	Created	User	Rating <<	#runs
run	xterm-knnsvd_shrunk_unbias_rev-r	2y84d ago	xterm	61	6
run	MyMediaLite-slopeone	217d21h ago	zenogantner	61	6
run	mean_user_rating_ruby	2y109d ago	jake	61	6
run	mean_movie_rating_ruby	2y105d ago	jake	61	6
run	MyMediaLite-user-item-baseline	216d19h ago	zenogantner	60	16
run	jjariyas-svdj-r	2y88d ago	jjariyas	60	6
run	nanhengwu-mf_for1m2-r	2y87d ago	nanhengwu	60	7
run	MyMediaLite-matrix-factorization-k-10	217d4h ago	zenogantner	60	19
run	MyMediaLite-matrix-factorization-k-20	217d4h ago	zenogantner	60	19
run	MyMediaLite-matrix-factorization-k-40	217d4h ago	zenogantner	60	19
run	MyMediaLite-matrix-factorization-k-5	217d4h ago	zenogantner	59	19
run	simple-knn-with-tuning-and-shrunk-column	323d18h ago	yorinasub17	59	7
run	whoburg-ALS_K1-r	2y87d ago	whoburg	59	6
run	cberner-KNN-r	2y91d ago	cberner	59	6
run	MyMediaLite-biased-matrix-factorization-k-60	217d4h ago	zenogantner	59	10
run	mjanderson-knn-r	2y88d ago	mjanderson	59	6
run	MyMediaLite-biased-matrix-factorization-k-40	217d4h ago	zenogantner	58	10
run	MyMediaLite-biased-matrix-factorization-k-20	217d19h ago	zenogantner	58	11
run	MyMediaLite-user-average	217d21h ago	zenogantner	58	6
run	mean_user_rating_ruby_2	2y105d ago	jake	58	6
run	lester-shrunk_item_mean-r	2y101d ago	lester	58	15
run	lester-shrunk_item_mean-testSunday	2y91d ago	jjariyas	58	15
run	MyMediaLite-biased-matrix-factorization-k-10	217d4h ago	zenogantner	58	10
run	MyMediaLite-biased-matrix-factorization-k-5	217d4h ago	zenogantner	58	10
run	cberner-KNNImproved-r	2y89d ago	cberner	58	6

1. pagination_page => 0
2. current_sort_col => avg_percentile
3. paginate => true
4. model => Program
5. name => programs
6. limit => 25
7. reverse_sort => true
8. show_footer => true
9. pagination_maxed => false
10. columns => prg_create_runprg_nameprg_created_atprg_userprg_avg_percentileprg_num_runs
11. width => 100%
12. joins =>
13. include => uservresult
14. title => Select a program
15. filters => is_helperfalseprocess_statussuccesstask_typeCollaborativeFiltering

Existing runs on eachmovie (all) 1-25 of 117 < > Filter:

ID	Program	Dataset	Tuned hyper.	User	Updated	Status	Total time	Memory	Error >>
Run #14860	MyMediaLite-biased-matrix-factorization-k-10	eachmovie	no	internal	208d6h ago	done	1h15m	433M	1.13
Run #14757	MyMediaLite-matrix-factorization-k-10	eachmovie	no	internal	208d16h ago	done	17m54s	428M	1.13
Run #14865	MyMediaLite-biased-matrix-factorization-k-5	eachmovie	no	internal	208d6h ago	done	59m57s	433M	1.13
Run #14729	MyMediaLite-matrix-factorization-k-60	eachmovie	no	internal	208d16h ago	done	47m55s	434M	1.13
Run #14850	MyMediaLite-matrix-factorization-k-20	eachmovie	no	internal	208d15h ago	done	24m42s	433M	1.14
Run #14771	MyMediaLite-matrix-factorization-k-40	eachmovie	no	internal	208d16h ago	done	34m56s	435M	1.14
Run #14845	MyMediaLite-biased-matrix-factorization-k-20	eachmovie	no	internal	208d6h ago	done	1h41m	433M	1.14
Run #14842	MML-BMF	eachmovie	no	internal	208d16h ago	done	17m45s	434M	1.15
Run #14855	MyMediaLite-biased-matrix-factorization-k-60	eachmovie	no	internal	208d4h ago	done	3h36m	433M	1.15
Run #14743	MyMediaLite-matrix-factorization-k-5	eachmovie	no	internal	208d16h ago	done	10m37s	433M	1.15
Run #14870	MyMediaLite-biased-matrix-factorization-k-40	eachmovie	no	internal	208d5h ago	done	2h30m	433M	1.16
Run #1333	lester-mf_rsgd-r	eachmovie	no	jake	1y122d ago	done	1h26m	440M	1.17
Run #14095	MyMediaLite-matrix-factorization-k-10	eachmovie	yes	zenogantner	217d2h ago	done	49m15s	438M	1.17
Run #14099	MyMediaLite-matrix-factorization-k-5	eachmovie	yes	zenogantner	217d2h ago	done	43m27s	438M	1.17
Run #1233	mjanderson-matrix-factorization-r	eachmovie	no	lester	1y122d ago	done	51m0s	1266M	1.17
Run #14083	MyMediaLite-matrix-factorization-k-60	eachmovie	yes	zenogantner	217d1h ago	done	2h36m	438M	1.18
Run #14091	MyMediaLite-matrix-factorization-k-20	eachmovie	yes	zenogantner	217d3h ago	done	1h9m	438M	1.18
Run #14087	MyMediaLite-matrix-factorization-k-40	eachmovie	yes	zenogantner	217d2h ago	done	1h55m	438M	1.19
Run #1264	xterm-knnsvd_shrunk_unbias_rev-r	eachmovie	no	xterm	1y122d ago	done	4h39m	1887M	1.19
Run #1231	cberner-KNNImproved-r	eachmovie	no	lester	1y122d ago	done	1h28m	1764M	1.20
Run #13990	MyMediaLite-slopeone	eachmovie	no	zenogantner	217d20h ago	done	25m23s	434M	1.25
Run #14000	MyMediaLite-bipolar-slopeone	eachmovie	no	zenogantner	217d20h ago	done	29m53s	496M	1.25
Run #14895	MyMediaLite-user-item-baseline	eachmovie	no	internal	208d14h ago	done	2m2s	433M	1.26
Run #2214	whoburg-ALS_K1-r	eachmovie	no	internal	1y122d ago	done	1h35m	429M	1.27
Run #1266	nanhengwu-mf_for1m2-r	eachmovie	no	nanhengwu	1y122d ago	done	1h25m	499M	1.29

1. pagination_page => 0
2. filter_field => programs.namedatasets.nameruns.idrun_statuses.status
3. current_sort_col => error
4. paginate => true
5. model => Run
6. name => runs
7. limit => 25
8. show_footer => true
9. reverse_sort => false
10. pagination_maxed => false
11. columns => run_idrun_core_programrun_core_datasetrun_hyperrun_userrun_updated_atrun_statusrun_timerun_memoryrun_error
12. width => 100%
13. joins => statuscore_programcore_dataset
14. filters => core_dataset_id350programs.is_helperfalse
15. title => Existing runs on eachmovie

Processing details

• exitCode: 0
• success: true
• test:
  • maxLabelValue: 6.0
  • minLabelValue: 1.0
  • numCols: 1623
  • numExamples: 29998
  • numRows: 30000
  • success: true
  • time: 0
• train:
  • maxLabelValue: 6.0
  • minLabelValue: 1.0
  • numCols: 1648
  • numExamples: 2079628
  • numRows: 30000
  • success: true
  • time: 9

Comments:

• 

  Yicong Liang (lycong)
  City University of Hong Kong
  Posted at 01/08/2011

  about how to download EachMovie dataset

  Can you guys send me a copy of EachMovie dataset to me? liangyicong@gmail.com Thanks Yicong
• 

  Jacob Abernethy (jake)
  UC Berkeley Comp Sci
  Posted at 11/15/2009

  Hello there new dataset

  Lester has apparently decided to run a million things now that eachmovie is up