This is prototype code for research purposes only.

The code implements a trainable Poisson process simulator for interactive systems. The purpose of this repository is to provide the implementation used in the public experiments described in the RecSys '21 publication. See that paper for more details on the modeling and inference steps.

There are two modules:

• simtrain -> contains core logic for building the simulator and training on observed data
• experiment -> enables experiments using a trained simulator

There are three notebooks:

• notebooks/process_contentwise_data.ipynb -> notebook to process the ContentWise dataset into a format that can be used to train the simulator
• notebooks/train_simulator.ipynb -> notebook demonstrating how the simulator is trained
• notebooks/boltzmann_study.ipynb -> notebook to make a Boltzmann exploration hyperparameter sweep using simulation

1. Download the ContentWise dataset please use the CSV data from their folder ContentWiseImpressions/data/ContentWiseImpressions/CW10M-CSV/
2. Set your base paths in the file notebooks/paths.py to point to where you downloaded the data and where intermediary and processed files will live
3. Adjust the simtrain.SETTINGS.py file according to the available computational resources (i.e. N_SUBSAMPLE_USERS, NUMEXPR_MAX_THREADS). Please be aware, the initial loading and first step processing of the data in process_contentwise_data.ipynb takes a lot of time and memory (~4 hours on large single instance). Beyond this point, user subsampling results in a smaller system of users and items that helps speed up the code for the proof of concept.

Requires the following packages:

numpy>=1.18.2

pandas>=0.25.3

matplotlib>=3.2.1

scipy>=1.4.1

scikit-learn>=0.22.2

tensorflow>=1.15.0

grpcio>=1.24.3

tqdm>4.62.2

Cite as:

@inproceedings{mcinerney2021accordion, title={Accordion: A Trainable Simulator for Long-Term Interactive Systems}, author={McInerney, James and Elahi, Ehtsham and Basilico, Justin and Raimond, Yves and Jebara, Tony}, booktitle={Fifteenth ACM Conference on Recommender Systems}, pages={102--113}, year={2021} }