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RecSim: A Configurable Recommender Systems Simulation Platform (w/o Tensorflow deps)

Project description

RecSim: A Configurable Recommender Systems Simulation Platform

RecSim is a configurable platform for authoring simulation environments for recommender systems (RSs) that naturally supports sequential interaction with users. RecSim allows the creation of new environments that reflect particular aspects of user behavior and item structure at a level of abstraction well-suited to pushing the limits of current reinforcement learning (RL) and RS techniques in sequential interactive recommendation problems. Environments can be easily configured that vary assumptions about: user preferences and item familiarity; user latent state and its dynamics; and choice models and other user response behavior. We outline how RecSim offers value to RL and RS researchers and practitioners, and how it can serve as a vehicle for academic-industrial collaboration.


This is not an officially supported Google product.

What's new

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

Installation and Sample Usage

It is recommended to install RecSim using (

pip install recsim

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

pip install git+

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

git clone
cd recsim/recsim
python --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 \

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


To get started, please check out our Colab tutorials. In RecSim: Overview, we give a brief overview about RecSim. We then talk about each configurable component: environment and recommender agent.


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

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