RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems

Related tags

Recommender Systems google reinforcement-learning simulation tensorflow artificial-intelligence probabilistic-programming recommender-system
Overview

RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems

RecSim NG, a probabilistic platform for multi-agent recommender systems simulation. RecSimNG is a scalable, modular, differentiable simulator implemented in Edward2 and TensorFlow. It offers: a powerful, general probabilistic programming language for agent-behavior specification; an XLA-based vectorized execution model for running simulations on accelerated hardware; and tools for probabilistic inference and latent-variable model learning, backed by automatic differentiation and tracing. We describe RecSim NG and illustrate how it can be used to create transparent, configurable, end-to-end models of a recommender ecosystem. Specifically, we present a collection of use cases that demonstrate how the functionality described above can help both researchers and practitioners easily develop and train novel algorithms for recommender systems. Please refer to Mladenov et al for the high-level design of RecSim NG. Please cite the paper if you use the code from this repository in your work.

Bibtex

@article{mladenov2021recsimng,
    title = {RecSim {NG}: Toward Principled Uncertainty Modeling for Recommender Ecosystems},
    author = {Martin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier}
    year = {2021},
    eprint={2103.08057},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

Disclaimer

This is not an officially supported Google product.

Installation and Sample Usage

It is recommended to install RecSim NG using (https://pypi.org/project/recsim_ng/). We want to install the latest version from Edward2's repository:

pip install recsim_ng
pip install -e "git+https://github.com/google/edward2.git#egg=edward2"

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

git clone https://github.com/google-research/recsim_ng
cd recsim_ng/recsim_ng/applications/ecosystem_simulation
python ecosystem_simulation_demo.py

Tutorials

To get started, please check out our Colab tutorials. In RecSim NG: Basics, we introduce the RecSim NG model and corresponding modeling APIs and runtime library. We then demonstrate how we define a simulation using entities, behaviors, and stories. Finally, we illustrate differentiable simulation including model learning and inference.

In RecSim NG: Dealing With Uncertainty, we explicitly address the stochastics of the Markov process captured by a DBN. We demonstrate how to use Edward2 in RecSim NG and show how to use the corresponding RecSim NG APIs for inference and learning tasks. Finally, we showcase how the uncertainty APIs of RecSim NG can be used within a recommender-system model-learning application.

Documentation

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

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Comments
  • TFDataset trajectories - issue with ownedIterator

    TFDataset trajectories - issue with ownedIterator

    recsim_ng 0.1.2, tfp 0.17, tensorflow 2.9.0

    Trying to create a trajectory from a tf dataset data_variable causes an error.

    tf_dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3])
x = data.data_variable(
    name="x",
    spec=ValueSpec(a=FieldSpec()),
    data_sequence=TFDataset(dataset=tf_dataset),
    output_fn=lambda t: Value(a=t * t)
)
r = runtime.TFRuntime(network=network_lib.Network(variables=[x]), graph_compile=False)
traj = r.trajectory(length=3)

    ValueError: Attempt to convert a value (<tensorflow.python.data.ops.iterator_ops.OwnedIterator object at 0x162105910>) with an unsupported type (<class 'tensorflow.python.data.ops.iterator_ops.OwnedIterator'>) to a Tensor.

    execute still seems to work fine, per recsim_ng's own DataTest, so I think it's possible to work around this by composing a trajectory out of successive executes.

    opened by ckchow 0
  • Incorrect implementation of REINFORCE algorithm

    Incorrect implementation of REINFORCE algorithm

    last_state = tf_runtime.execute(num_steps=horizon - 1) last_metric_value = last_state['metrics state'].get(metric_to_optimize) log_prob = last_state['slate docs_log_prob_accum'].get('doc_ranks') objective = -tf.tensordot(tf.stop_gradient(last_metric_value), log_prob, 1)

    In this above code snippet (interest_evolution_simulation.py, line 45-48), the cumulative_log_prob (across the entire episode) is being weighted by the total cumulative reward from the start to the end of the episode. This seems incorrect as the log_prob at step 't' should be weighted by cumulative reward from step 't' onwards (not from the start).

    opened by ehtsham 5
  • Cannot run interest_evolution_simulation_demo.py

    Cannot run interest_evolution_simulation_demo.py

    I am trying to run interest_evolution_simulation_demo.py to test my installation. I am getting a bunch of warnings and no results are popping up from running the demo. I am guessing this is due to tensorflow version, but not sure.

    prompt from running interest_evolution_simulation_demo.py

    UserWarning: NumPy backend for Edward2 is not available.
  warnings.warn("NumPy backend for Edward2 is not available.")
  2022-03-16 22:10:35.450846: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
WARNING:tensorflow:AutoGraph could not transform <bound method IteratedMultinomialLogitChoiceModel.choice of <recsim_ng.entities.choice_models.selectors.IteratedMultinomialLogitChoiceModel object at 0x7f985178ddf0>> and will run it as-is.
Cause: generators are not supported
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
W0316 22:10:37.136438 140294645813824 ag_logging.py:142] AutoGraph could not transform <bound method IteratedMultinomialLogitChoiceModel.choice of <recsim_ng.entities.choice_models.selectors.IteratedMultinomialLogitChoiceModel object at 0x7f985178ddf0>> and will run it as-is.
Cause: generators are not supported
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
2022-03-16 22:10:42.510012: W tensorflow/core/grappler/costs/op_level_cost_estimator.cc:690] Error in PredictCost() for the op: op: "Softmax" attr { key: "T" value { type: DT_FLOAT } } inputs { dtype: DT_FLOAT shape { unknown_rank: true } } device { type: "CPU" vendor: "AuthenticAMD" model: "241" frequency: 2500 num_cores: 128 environment { key: "cpu_instruction_set" value: "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 32768 l2_cache_size: 524288 l3_cache_size: 134217728 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { unknown_rank: true } }
2022-03-16 22:10:42.510178: W tensorflow/core/grappler/costs/op_level_cost_estimator.cc:690] Error in PredictCost() for the op: op: "Softmax" attr { key: "T" value { type: DT_FLOAT } } inputs { dtype: DT_FLOAT shape { unknown_rank: true } } device { type: "CPU" vendor: "AuthenticAMD" model: "241" frequency: 2500 num_cores: 128 environment { key: "cpu_instruction_set" value: "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 32768 l2_cache_size: 524288 l3_cache_size: 134217728 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { unknown_rank: true } }

    Below are the packages installed

    absl-py                      0.15.0
astunparse                   1.6.3
cachetools                   4.2.4
certifi                      2021.10.8
charset-normalizer           2.0.12
clang                        5.0
cloudpickle                  2.0.0
cycler                       0.11.0
decorator                    5.1.1
dm-tree                      0.1.6
edward2                      0.0.2
flatbuffers                  1.12
fonttools                    4.30.0
gast                         0.4.0
gin-config                   0.5.0
google-auth                  1.35.0
google-auth-oauthlib         0.4.6
google-pasta                 0.2.0
grpcio                       1.44.0
gym                          0.23.1
gym-notices                  0.0.6
h5py                         3.1.0
idna                         3.3
importlib-metadata           4.11.3
keras                        2.8.0
Keras-Preprocessing          1.1.2
kiwisolver                   1.4.0
libclang                     13.0.0
Markdown                     3.3.6
matplotlib                   3.5.1
numpy                        1.22.3
oauthlib                     3.2.0
opt-einsum                   3.3.0
packaging                    21.3
Pillow                       9.0.1
pip                          21.2.4
protobuf                     3.19.4
pyasn1                       0.4.8
pyasn1-modules               0.2.8
pyparsing                    3.0.7
python-dateutil              2.8.2
recsim-ng                    0.1.1
requests                     2.27.1
requests-oauthlib            1.3.1
rsa                          4.8
setuptools                   58.0.4
six                          1.15.0
tensorboard                  2.8.0
tensorboard-data-server      0.6.1
tensorboard-plugin-wit       1.8.1
tensorflow                   2.8.0
tensorflow-estimator         2.7.0
tensorflow-io-gcs-filesystem 0.24.0
tensorflow-probability       0.16.0
termcolor                    1.1.0
tf-estimator-nightly         2.8.0.dev2021122109
typing-extensions            3.10.0.2
urllib3                      1.26.8
Werkzeug                     2.0.3
wheel                        0.37.1
wrapt                        1.12.1
zipp                         3.7.0
    opened by soonjune 3
  • module 'recsim_ng.core.value' has no attribute 'Space'

    module 'recsim_ng.core.value' has no attribute 'Space'

    Hi, I'm following your tutorials, but encounter with one trouble...

    In "The User Model" part of second tutorial (RecSim_NG_Dealing_With_Uncertainty.ipynb), we need to do some imports, as belows:

    from recsim_ng.stories import recommendation_simulation as simulation
from recsim_ng.applications.latent_variable_model_learning import recommender
from recsim_ng.entities.recommendation import user
from recsim_ng.entities.state_models import static
from recsim_ng.entities.choice_models import selectors
from recsim_ng.entities.choice_models import affinities

    But the second import throw an error as belows:

    AttributeError                            Traceback (most recent call last)
<ipython-input-5-d9a555230cf0> in <module>
      1 # @title RecSimNG modeling imports.
      2 from recsim_ng.stories import recommendation_simulation as simulation
----> 3 from recsim_ng.applications.latent_variable_model_learning import recommender
      4 from recsim_ng.entities.recommendation import user
      5 from recsim_ng.entities.state_models import static

/usr/local/anaconda3/lib/python3.7/site-packages/recsim_ng/applications/latent_variable_model_learning/__init__.py in <module>
     15 
     16 """Module importing the application learning latent variable models."""
---> 17 from recsim_ng.applications.latent_variable_model_learning import recommender
     18 from recsim_ng.applications.latent_variable_model_learning import simulation_config
     19 from recsim_ng.applications.latent_variable_model_learning import user

/usr/local/anaconda3/lib/python3.7/site-packages/recsim_ng/applications/latent_variable_model_learning/recommender.py in <module>
     27 Value = value.Value
     28 ValueSpec = value.ValueSpec
---> 29 Space = value.Space
     30 
     31 

AttributeError: module 'recsim_ng.core.value' has no attribute 'Space'

    And I checked the source code then and indeed, I can't find "Space" Class in recsim_ng.core.value

    Looking forward to your kindly help.

    opened by aSeriousCoder 4
Owner
Google Research
Google Research
GitHub https://github.com/google-research/recsim_ng
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