Metadata-Version: 2.1
Name: recsim
Version: 0.2.4
Summary: RecSim: A Configurable Recommender Systems Simulation Platform
Home-page: https://github.com/google-research/recsim
Author: The RecSim Team
Author-email: no-reply@google.com
License: Apache 2.0
Project-URL: Source, https://github.com/google-research/recsim
Project-URL: Documentation, https://github.com/google-research/recsim
Project-URL: Bug Reports, https://github.com/google-research/recsim/issues
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. For a detailed description of the RecSim
        architecture please read [Ie et al](https://arxiv.org/abs/1909.04847). Please
        cite 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}
        }
        ```
        
        <a id='Disclaimer'></a>
        
        ## Disclaimer
        
        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 (https://pypi.org/project/recsim/):
        
        ```shell
        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+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. In
        [**RecSim: Overview**](recsim/colab/RecSim_Overview.ipynb),
        we give a brief overview about RecSim. We then talk about each configurable
        component:
        [**environment**](recsim/colab/RecSim_Developing_an_Environment.ipynb)
        and
        [**recommender agent**](recsim/colab/RecSim_Developing_an_Agent.ipynb).
        
        ## Documentation
        
        
        Please refer to the [white paper](http://arxiv.org/abs/1909.04847) for the
        high-level design.
        
Keywords: recsim reinforcement-learning recommender-system simulation
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
