wikirec 1.0.0

• Release switches wikirec over to semantic versioning and indicates that it is stable

wikirec 0.2.2

Changes include:

• The WikilinkNN model has been added allowing users to derive recommendations based which articles are linked to the same other Wikipedia articles
• Examples have been updated to reflect this new model
• books_embedding_model.h5 is provided for quick experimentation
• enwiki_books.ndjson has been updated with a more recent dump
• Function docstring grammar fixes
• Baseline testing for the new model has been added to the CI

wikirec 0.2.1

Changes include:

• Support has been added for gensim 3.8.x and 4.x
• Wikipedia links are now an output of data_utils.parse_to_ndjson
• Dependencies in requirement and environment files are now condensed

wikirec 0.2.0

Changes include:

• Users can now input ratings to weigh recommendations
• Fixes for how multiple inputs recommendations were being calculated
• Switching over to an src structure
• Code quality is now checked with Codacy
• Extensive code formatting to improve quality and style
• Bug fixes and a more explicit use of exceptions
• More extensive contributing guidelines

wikirec 0.1.1.7

Changes include:

• Multiple Infobox topics can be subsetted for at the same time

• Users have greater control of the cleaning process

• The cleaning process is verbose and uses multiprocessing

• The workflow for all models has been improved and explained

• Methods have been developed to combine modeling techniques for better results

wikirec 0.1.0

wikirec 0.1.0 (March 8, 2021)

First stable release of wikirec

• Functions to subset Wikipedia in any language by infobox topics have been provided

• A multilingual cleaning process that can clean texts of any language to varying degrees of efficacy is included

• Similarity matrices can be generated from embeddings using the following models:

  • BERT
  • Doc2vec
  • LDA
  • TFIDF

• Similarity matrices can be created using either cosine or euclidean relations

• Usage examples have been provided for multiple input types

• Optimal LDA topic numbers can be inferred graphically

• The package is fully documented

• Virtual environment files are provided

• Extensive testing of all modules with GH Actions and Codecov has been performed

• A code of conduct and contribution guidelines are included