Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagement

Comments are welcome! 🦊🐇

Environment setup

conda create -n lvml anaconda
conda activate lvml
conda install -c conda-forge scikit-surprise

run_experiments.ipynb - Run and analyze MovieLens/Goodreads experients. Results are written to ./output.
analyze_experiment.ipynb - Reproduce experiment analysis. Analysis of each dataset was performed on random seeds [1,...,10].
schematic_diagrams.ipynb - Reproduce schematic diagrams.
prepare_goodreads_dataset.ipynb - Prepare the Goodreads dataset for analysis.
lvml/core/simulation.py - Core simulation code, TPP simulator, ODE solver wrapper.
lvml/core/policy.py - Policy optimizers (LV, best-of, default).
lvml/experiment/* - Experiment-related utilities.

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
lvml		lvml
README.md		README.md
experiment_analysis.ipynb		experiment_analysis.ipynb
prepare_goodreads_dataset.ipynb		prepare_goodreads_dataset.ipynb
run_experiments.ipynb		run_experiments.ipynb
run_nb.sh		run_nb.sh
schematic_diagrams.ipynb		schematic_diagrams.ipynb