RecSim: A Configurable Recommender Systems Simulation Platform

RecSim is a configurable platform for authoring simulation environments forrecommender systems (RSs) that naturally supports sequential interactionwith users. RecSim allows the creation of new environments that reflectparticular aspects of user behavior and item structure at a level of abstractionwell-suited to pushing the limits of current reinforcement learning (RL) and RStechniques in sequential interactive recommendation problems. Environments canbe easily configured that vary assumptions about: user preferences and itemfamiliarity; user latent state and its dynamics; and choice models and otheruser response behavior. We outline how RecSim offers value to RL and RSresearchers and practitioners, and how it can serve as a vehicle foracademic-industrial collaboration. For a detailed description of the RecSimarchitecture please read Ie et al. Pleasecite the paper if you use the code from this repository in your work.

Bibtex

@article{ie2019recsim,
    title={RecSim: A Configurable Simulation Platform for Recommender Systems},
    author={Eugene Ie and Chih-wei Hsu and Martin Mladenov and Vihan Jain and Sanmit Narvekar and Jing Wang and Rui Wu and Craig Boutilier},
    year={2019},
    eprint={1909.04847},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

Disclaimer

This is not an officially supported Google product.

What's new

• 12/13/2019: Added (abstract) classes for both multi-user environmentsand agents. Added bandit algorithms for generalized linear models.

Installation and Sample Usage

It is recommended to install RecSim using (https://pypi.org/project/recsim/):

pip install recsim

However, the latest version of Dopamine is not in PyPI as of December, 2019. Wewant to install the latest version from Dopamine's repository like the followingbefore we install RecSim. Note that Dopamine requires Tensorflow 1.15.0 which isthe final 1.x release including GPU support for Ubuntu and Windows.

pip install git+https://github.com/google/dopamine.git

Here are some sample commands you could use for testing the installation:

git clone https://github.com/google-research/recsim
cd recsim/recsim
python main.py --logtostderr \
  --base_dir="/tmp/recsim/interest_exploration_full_slate_q" \
  --agent_name=full_slate_q \
  --environment_name=interest_exploration \
  --episode_log_file='episode_logs.tfrecord' \
  --gin_bindings=simulator.runner_lib.Runner.max_steps_per_episode=100 \
  --gin_bindings=simulator.runner_lib.TrainRunner.num_iterations=10 \
  --gin_bindings=simulator.runner_lib.TrainRunner.max_training_steps=100 \
  --gin_bindings=simulator.runner_lib.EvalRunner.max_eval_episodes=5

You could then start a tensorboard and view the output

tensorboard --logdir=/tmp/recsim/interest_exploration_full_slate_q/ --port=2222

You could also find the simulated logs in /tmp/recsim/episode_logs.tfrecord

Tutorials

To get started, please check out our Colab tutorials. InRecSim: Overview,we give a brief overview about RecSim. We then talk about each configurablecomponent:environmentandrecommender agent.

Documentation

Please refer to the white paper for thehigh-level design.