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