Collaborative filtering

Podium

Lowest RMSE wins. Student groups and professor references compete together.

1

ONN

0.789 RMSE

2

SnapCat

0.795 RMSE

3

Breaking Bias

0.799 RMSE

RMSE over time · lower is better.

Drag to zoom; use the toolbar to pan or reset. Dots are evaluations, not GitHub activity. Failed runs leave gaps.

Scores use hidden evaluation ratings; the input contains train + test. Accuracy is the percentage of exactly predicted ratings. These measurements support your analysis; code itself is not graded.

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Rank	Group ↕	RMSE ↑	Accuracy (%) ↕	Time (s) ↕	Status	Source
1	ONN2026 group	0.789	0.13	1.02	Success	Repository7de682f7
2	SnapCat2026 group	0.795	0.00	31.14	Success	Repositoryefa5475d
3	Breaking Bias2026 group	0.799	0.25	17.47	Success	Repositorybcfada45
4	HBA2026 group	0.799	0.16	216.72	Success	Repositorya4daa4cd
5	Group 162026 group	0.799	0.28	132.35	Success	Repository766b27b7
6	La piscine2026 group	0.801	0.21	138.73	Success	Repository4e7adb00
7	Group 132026 group	0.803	0.21	20.47	Success	Repositorya5927f2a
8	LHOOQ2026 group	0.806	0.17	121.15	Success	Repository87694bf0
9	Le Groupe2026 group	0.808	0.00	256.66	Success	Repositoryb630569e
10	rEcommeNderS2026 group	0.811	0.09	265.30	Success	Repositoryef1b25cd
11	altf42026 group	0.813	0.07	107.63	Success	Repository85cea6cb
12	Le groupe du fond : LiS2026 group	0.815	0.03	386.00	Success	Repository6e09fd98
13	BestOf 2025 · 1Reference	0.816	0.00	480.95	Success	Frozen referenceab53d900
14	Group 312026 group	0.818	0.35	74.06	Success	Repositoryef06eee3
15	BANs2026 group	0.824	0.09	65.03	Success	Repository3b683043
16	BestOf 2025 · 2Reference	0.830	0.02	4.78	Success	Frozen reference55d5e886
17	AcceleratedAerodynamicsYaris2026 group	0.831	0.03	207.56	Success	Repositoryfdfe5a3c
18	Blocus2026 group	0.833	0.09	4.63	Success	Repository134e901b
19	DauDau2026 group	0.833	0.14	71.83	Success	Repository1438069b
20	Group 72026 group	0.839	0.05	966.91	Success	Repository7110d9bd
21	blue2026 group	0.839	0.11	5.83	Success	Repository4377f45c
22	Rejoignez@Alexis@Hadrien2026 group	0.841	0.10	7.94	Success	Repositorycf3f11e5
23	Group 282026 group	0.843	0.04	201.45	Success	Repositoryebba51c7
24	MassSurveillanceReadyFiltering2026 group	0.846	0.14	9.09	Success	Repositorye0bd9939
25	WorksOnMyMachine2026 group	0.847	0.10	1.77	Success	Repositoryd96878d4
26	Group2026 group	0.848	0.00	11.25	Success	Repository3b85f316
27	ACA2026 group	0.848	0.06	53.14	Success	Repository5caded6e
28	AKE2026 group	0.848	0.09	11.60	Success	Repositoryc60ef494
29	Group 42026 group	0.848	0.00	1025.72	Success	Repositoryac15a8da
30	Gengis GAN2026 group	0.849	0.00	32.64	Success	Repository848e0353
31	BestOf 2024 · 1Reference	0.850	0.00	148.24	Success	Frozen reference1204cccc
32	ocaml2026 group	0.854	0.14	3.73	Success	Repositoryc6ec8398
33	force-push2026 group	0.857	0.08	4.48	Success	Repository11e7fd1d
34	data-doodlers2026 group	0.858	0.06	22.77	Success	Repository2380252a
35	Antoine2026 group	0.859	0.10	1.87	Success	Repository255e0811
36	Group 372026 group	0.859	0.06	34.19	Success	Repository36c0a75a
37	BestOf 2023 · 2Reference	0.860	26.12	87.98	Success	Frozen reference2fa4d93a
38	TinderCEOs2026 group	0.878	0.02	5.88	Success	Repositoryf2ec7e8d
39	The Outliers2026 group	0.878	0.34	1.77	Success	Repository71bb1a7e
40	Cold Start Club2026 group	1.037	0.00	1.02	Success	Repository17d84449
41	UsualGreenSundayEnjoyer2026 group	1.037	0.00	0.87	Success	Repositoryb717a243
42	averageReference	1.037	0.00	1.02	Success	Frozen reference
43	randomReference	1.820	14.17	0.97	Success	Frozen reference
—	1p2v2026 group	—	—	—	Error !execution: RuntimeError: Process exited with code 1. Traceback (most recent call last): File "/work/generate.py", line 6, in <module> from compareKnn import SimilarityFusion File "/work/compareKnn.py", line 3, in <module> from numba import njit, prange ModuleNotFoundError: No module named 'numba'	Repository1f3865cc
—	BestOf 2023 · 1Reference	—	—	—	Error !execution: RuntimeError: Process exited with code 1. Ratings loading... Ratings Loaded. Traceback (most recent call last): File "/work/generate.py", line 38, in <module> model.load_best_models() ~~~~~~~~~~~~~~~~~~~~~~^^ File "/work/algorithms/folded_deep_matrix_factorization_imprv.py", line 327, in load_best_models m.load_state_dict(torch.load(abs_file_path)) ~~~~~~~~~~^^^^^^^^^^^^^^^ File "/env/lib/python3.13/site-packages/torch/serialization.py", line 1521, in load return _load( opened_zipfile, ...<3 lines>... **pickle_load_args, ) File "/env/lib/python3.13/site-packages/torch/serialization.py", line 2119, in _load result = unpickler.load() File "/env/lib/python3.13/site-packages/torch/_weights_only_unpickler.py", line 532, in load self.append(self.persistent_load(pid)) ~~~~~~~~~~~~~~~~~~~~^^^^^ File "/env/lib/python3.13/site-packages/torch/serialization.py", line 2083, in persistent_load typed_storage = load_tensor( dtype, nbytes, key, _maybe_decode_ascii(location) ) File "/env/lib/python3.13/site-packages/torch/serialization.py", line 2049, in load_tensor wrap_storage = restore_location(storage, location) File "/env/lib/python3.13/site-packages/torch/serialization.py", line 698, in default_restore_location result = fn(storage, location) File "/env/lib/python3.13/site-packages/torch/serialization.py", line 636, in _deserialize device = _validate_device(location, backend_name) File "/env/lib/python3.13/site-packages/torch/serialization.py", line 605, in _validate_device raise RuntimeError( ...<5 lines>... ) RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.	Frozen reference8b6fb772
—	BestOf 2024 · 2Reference	—	—	—	Error !execution: RuntimeError: Process exited with code 1. /work/generate.py:57: DeprecationWarning: __array_wrap__ must accept context and return_scalar arguments (positionally) in the future. (Deprecated NumPy 2.0) ratings = np.where(~np.isnan(matrix[i]))[0] Ratings loading... Ratings Loaded. (array([False, True]), array([2974604, 63196])) Traceback (most recent call last): File "/work/generate.py", line 121, in <module> previous_pred = torch.zeros(ratings_full.shape).to(device) File "/env/lib/python3.13/site-packages/torch/cuda/__init__.py", line 412, in _lazy_init torch._C._cuda_init() ~~~~~~~~~~~~~~~~~~~^^ RuntimeError: No CUDA GPUs are available	Frozen reference7869cd93
—	Group 272026 group	—	—	—	Pending	Repository pending
—	Matcha2026 group	—	—	—	Pending	Repository pending
—	OT dream team2026 group	—	—	—	Error !execution: RuntimeError: Process exited with code 1. Traceback (most recent call last): File "/work/generate.py", line 5, in <module> from matrix_factorization_GD import complete_ratings_table ModuleNotFoundError: No module named 'matrix_factorization_GD'	Repositoryad2f13c6
—	umap reborn2026 group	—	—	—	Error !execution: RuntimeError: Process exited with code 1. Ratings loading... Ratings Loaded. Traceback (most recent call last): File "/work/generate.py", line 25, in <module> table = complete(table) File "/work/methods/__init__.py", line 57, in complete return create_method(method, **params).complete(table) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^ File "/work/methods/base.py", line 82, in complete predictions = check_predictions(self._predict(table, observed), table.shape) ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ File "/work/methods/nlpca.py", line 398, in _predict return NonlinearPCA(**params).fit(table).predict(clip=False) ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^ File "/work/methods/nlpca.py", line 222, in fit item_features, self.feature_names_ = load_movie_features( ~~~~~~~~~~~~~~~~~~~^ n_items, movie_ids=movie_ids, use_genres=self.use_genres, ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ use_year=self.use_year, use_title_tokens=self.use_title_tokens, ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ title_min_count=self.title_min_count, ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ year_center=self.year_center, year_scale=self.year_scale) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/work/methods/movie_features.py", line 114, in load_movie_features metadata, aligned_ids = load_movie_metadata(root=ROOT) ~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^ File "/work/models/movie_metadata.py", line 34, in load_movie_metadata raise FileNotFoundError(f"Missing official movie metadata: {path}") FileNotFoundError: Missing official movie metadata: /work/data/namesngenre.npy	Repository1939077d

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