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Fast Python Collaborative Filtering for Implicit Feedback Datasets

active 2024-10-132026-05-24 (UTC)

Partial coverage13,446 / 16,028 hourly files (84%) · 2 absent upstream · 2,576 failed, retryable2024-10-122026-08-11 (UTC)— sampled evenly across the window, so rankings and trends hold; absolute counts scale up.
Events
201
Pushes
21
Pull requests
5
Issues
6
Stars
138
Forks
11

Activity over time

Daily event counts in the loaded window

Line chart, 589 days from 2024-10-13 to 2026-05-24. Pushes: 21 total, peak 5 in a day. Pull requests: 5 total, peak 1 in a day. Issues: 6 total, peak 1 in a day. Comments: 13 total, peak 2 in a day. Stars: 138 total, peak 3 in a day.

  • Pushes
  • Pull requests
  • Issues
  • Comments
  • Stars

Stars, PRs, issues and forks are under-captured in the later part of this window. GH Archive progressively stopped capturing non-push events during 2026 — −95% or worse by the end of the window. Every series here except Pushes fades for that reason, so a decline above reflects the archive, not this repository. Pushes stay reliable throughout, so read them, and the contributor counts derived from them, as the real signal. Data health has the measurements.

Top contributors

Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity

ContributorContributionsPushesPRsComments
benfred252130
fkurushin4003
filiptrivan3002
jmorlock2011
Henddher1001
lyanv1000
jarandaf1001
vrana1010
chezou1001
w-toma1001
satsaras1000
levrone19871000
RoyceKipslin1000
sjain-ajc1001
lithammer1000
vivekpandian081001
Darinochka1001

Recent activity

Latest issues, pull requests and releases

  • Pull request#752benfred2026-05-05 23:20
  • Pull request#750benfred2026-05-03 00:09
  • Pull request#746benfred2026-05-02 00:17
  • Issue comment#728chezou2026-02-23 05:06
    CUDA 12 support
  • Issue comment#702Henddher2025-09-29 20:41
    Usage of the trained model on testing.
  • Pull request#739vrana2025-09-18 09:28
  • Issue comment#718jarandaf2025-08-22 13:09
    IMPLICIT: No CUDA extension has been built, can't train on GPU
  • Issue#738levrone19872025-06-13 10:50
    Assertion error (matrix transpose, user-item, item-user matrix)
  • Issue#736lyanv2025-05-10 12:38
    recommend(...) returns N + len(filter_items) results when filter_items is used
  • Issue comment#708sjain-ajc2025-04-22 02:22
    getting index error while using recommend function while ALS algorithm
  • Issue comment#332Darinochka2025-04-05 12:38
    ubuntu 18.04 w / gpu: No CUDA extension has been built, can't train on GPU.
  • Issue comment#733filiptrivan2025-03-19 00:30
    Ability to access final loss value after training completes in AlternatingLeastSquares
  • Issue#733filiptrivan2025-03-19 00:30
    Ability to access final loss value after training completes in AlternatingLeastSquares
  • Issue comment#281filiptrivan2025-03-19 00:28
    [HELP] Plot train loss for ALS, BPR using implicit?
  • Pull request#731jmorlock2025-01-25 21:41
  • Issue comment#726fkurushin2025-01-21 07:24
    Error `AttributeError: 'implicit.evaluation._memoryviewslice' object has no attribute 'dtype'` when calling `mean_average_precision_at_k` function
  • Issue#729fkurushin2025-01-17 21:41
    ValueError: Buffer dtype mismatch, expected 'int' but got 'long'
  • Issue comment#366fkurushin2025-01-11 08:09
    Multi-GPU Support
  • Issue comment#725jmorlock2025-01-04 20:13
    RuntimeError: Cuda Error: an illegal memory access was encountered (/project/implicit/gpu/als.cu:196)
  • Issue comment#716w-toma2024-12-28 10:52
    Potential bug in calculation of gradient updates for BPR
  • Issue comment#725fkurushin2024-12-28 09:39
    RuntimeError: Cuda Error: an illegal memory access was encountered (/project/implicit/gpu/als.cu:196)
  • Issue comment#725vivekpandian082024-11-17 06:07
    RuntimeError: Cuda Error: an illegal memory access was encountered (/project/implicit/gpu/als.cu:196)
  • Issue#724satsaras2024-10-19 18:51
    how als treats zero values
  • Issue#723RoyceKipslin2024-10-13 22:45
    Will not install using pip

Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 138 stars here means stars gained during the window, not the repo's star count.