sailist / SSP-MMC

A Stochastic Shortest Path Algorithm for Optimizing Spaced Repetition Scheduling

SSP-MMC

Copyright (c) 2022 MaiMemo, Inc. MIT License.

Stochastic-Shortest-Path-Minimize-Memorization-Cost (SSP-MMC) is a spaced repetition scheduling algorithm used to help learners remember more words in MaiMemo, a language learning application in China.

This repository contains a public release of the data and code used for several experiments in the following paper (which introduces SSP-MMC):

Junyao Ye, Jingyong Su, and Yilong Cao. 2022. A Stochastic Shortest Path Algorithm for Optimizing Spaced Repetition Scheduling. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 4381–4390. https://doi.org/10.1145/3534678.3539081

When using this data set and/or software, please cite this publication. A BibTeX record is:

@inproceedings{10.1145/3534678.3539081,
author = {Ye, Junyao and Su, Jingyong and Cao, Yilong},
title = {A Stochastic Shortest Path Algorithm for Optimizing Spaced Repetition Scheduling},
year = {2022},
publisher = {ACM},
doi = {10.1145/3534678.3539081},
pages = {4381–4390},
numpages = {10}
}

Software

The file data_preprocessing.py is used to preprocess data for the DHP model.

The file cal_model_param.py contains the DHP model and HLR model.

The file model/utils.py saves the parameters of the DHP model for training and simulation.

The file algo/main.cpp contains a Cpp implementation of SSP-MMC, which aims at finding the optimal policy.

The file simulator.py provides an environment for comparing different scheduling algorithms.

Workflow

1. Run data_preprocessing.py -> halflife_for_fit.tsv
2. Run cal_model_param.py -> intercept_ and coef_ for the DHP model
3. Save the parameters to the function cal_recall_halflife and cal_forget_halflife in model/utils.py and the function cal_next_recall_halflife in algo/main.cpp
4. Run algo/main.cpp -> optimal policy in algo/result/
5. Run simulator.py to compare the SSP-MMC with several baselines.

Data Set and Format

The dataset is available on Dataverse (1.6 GB). This is a 7zipped TSV file containing our experiments' 220 million MaiMemo student memory behavior logs.

The columns are as follows:

• u - student user ID who reviewed the word (anonymized)

• w - spelling of the word

• i - total times the user has reviewed the word

• d - difficulty of the word

• t_history - interval sequence of the historic reviews

• r_history - recall sequence of the historic reviews

• delta_t - time elapsed from the last review

• r - result of the review

• p_recall - probability of recall

• total_cnt - number of users who did the same memory behavior