Rsdiv: Reranking for Multi-objective Optimized Recommender Systems

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rsdiv provides the measurements and improvements for the multi-objective/diversifying tasks.

Some of its features include:

  • various implementations of diversifying/ensemble reranking modules.
  • various implementations of core recommender algorithms.
  • evaluations for recommender systems from a quantitative/visual view.
  • easy-to-use benchmarks for comparing and further analysis.
  • automated hyperparameter optimization.

Installation

You can simply install the pre-build binaries with:

pip install rsdiv

More installation options can be found here.

Basic Usage

Prepare for a benchmark dataset

Evaluate the results in various aspects

Train and test a recommender

Reranking for diversity improvement

TODO

More diversifying algorithms

  • implement the Bounded Greedy Selection Strategy, BGS diversify algorithm

  • implement the Determinantal Point Process, DPP diversify algorithm

Hyperparameter optimization

Ensemble ranking

  • support the ensemble ranking modules

For developers

Contributions welcome! Please contact us.

During your development stage, make sure you have pre-commit installed in your local environment:

pip install pre-commit
pre-commit install

Project links

Key dates

PyPI data
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  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for yin.cheng from gravatar.com yin.cheng

Credits

Author: Yin Cheng

Requires

Python >=3.8

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