implicit changelog

Changelog History

Page 1

• v0.5.0 Changes

  💥 Breaking API Changes

  The API for implicit has substantially changed in v0.5.0 - and any code written for the previous API will need to be rewritten:

  • 🔄 Change model.fit to take a user_items sparse matrix #484
  • Return numpy arrays from recommend methods #482
  • Don't require empty rows in user_items and item_users parameters #526
  • Unify API for rank_items/recommend/recommend_all #489

  🐎 Performance Improvements

  • Speedup evaluation by using batch recommend functions #520
  • 👉 Use FAISS for GPU inference #506
  • Multithreaded speedups for CPU models #517
  • 👉 Use thrust::binary_search to verify negative samples on GPU #524
  • 🚀 Release GIL on GPU calls #528

  🆕 New Features

  • ➕ Add incremental retraining support for ALS models #527
  • Add filtering for similar_items and similar_users #488
  • ➕ Add support for recalculate_users/items on the GPU #515
  • ➕ Add methods for converting MF models to/from gpu #521
  • ➕ Add a tutorial notebook for the lastfm example #529
  • Approximate nearest neighbour for BPR/LMF and GPU models #487
  • Dynamically detect CUDA availability #174

• v0.4.5 Changes

  • GPU Inference #406
  • 🛠 Fix ALS model for case of > 2³¹ interactions #400
  • 🛠 Fix GPU dot product when the # of factors wasn't warp aligned #427
  • 👉 Use gpu registers for dot product #448
  • 🛠 Fix random output with LMF/BPR models #408
  • ➕ Add seed for test-train split #411

• v0.4.4 Changes

  • ➕ Adds Precompiled CUDA packages on conda-forge
  • ⬇️ Drops support for CUDA 8

• v0.4.3 Changes

  • Implement filter_already_liked_items option #328
  • 🛠 Fix bug in ALS explain when user_items contains negative confidence values #313
  • 👌 Improve numerical stability of LMF #383
  • ➕ Add error check after training for NaN factors #381
  • 👌 Support building with Cuda 11

• v0.4.2

  October 23, 2019

• v0.4.1 Changes

  October 20, 2019

  • ➕ Add validation scheme(#283)[https://github.com/benfred/implicit/pull/283]

• v0.4.0 Changes

  August 01, 2019

  • ➕ Add logistic matrix factorization algorithm #231
  • 👉 Use tqdm for progress bars #240
  • ➕ Add AUC@K #275

• v0.3.9 Changes

  July 13, 2019

  • ➕ Add ability to pickle nearest neighbours recommenders (#191)[https://github.com/benfred/implicit/issues/191]
  • ➕ add NDCG method to evaluation (#212)[https://github.com/benfred/implicit/pull/212]
  • ➕ Add a 'recommend_all' method for matrix factorization models (#179)[https://github.com/benfred/implicit/pull/179]

• v0.3.8 Changes

  October 11, 2018

  • Ensure progress bar hits 100% during xval
  • 🛠 Fix bm25recommender missing default parameter on fit

• v0.3.7 Changes

  September 22, 2018

  • 🛠 Fix GPU faiss model with > 1024 results (#149)[https://github.com/benfred/implicit/issues/149]
  • ➕ Add a reddit votes dataseet
  • ➕ Add similar users calculation in MF modeles (#139)[https://github.com/benfred/implicit/pull/139]
  • ➕ Add an option to whether to include previously liked items or not (#131)[https://github.com/benfred/implicit/issues/131]
  • ➕ Add option for negative preferences to ALS modele (#119)[https://github.com/benfred/implicit/issues/119)
  • ➕ Add filtering negative feedback in test set (#124)[https://github.com/benfred/implicit/issues/124)

• 1
• 2
• Next ›
• Last »