======= RecList

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RecList

• Free software: MIT license
• Documentation: https://reclist.readthedocs.io.

Overview

RecList is an open source library providing behavioral, "black-box" testing for recommender systems. Inspired by the pioneering work of Ribeiro et al. 2020 <https://aclanthology.org/2020.acl-main.442.pdf>__ in NLP, we introduce a general plug-and-play procedure to scale up behavioral testing, with an easy-to-extend interface for custom use cases.

While quantitative metrics over held-out data points are important, a lot more tests are needed for recommenders to properly function in the wild and not erode our confidence in them: for example, a model may boast an accuracy improvement over the entire dataset, but actually be significantly worse than another on rare items or new users; or again, a model that correctly recommends HDMI cables as add-on for shoppers buying a TV, may also wrongly recommend TVs to shoppers just buying a cable.

RecList goal is to operationalize these important intuitions into a practical package for testing research and production models in a more nuanced way, without requiring unnecessary custom code and ad hoc procedures. To streamline comparisons among existing models, RecList ships with popular datasets and ready-made behavioral tests: read the TDS blog post <https://towardsdatascience.com/ndcg-is-not-all-you-need-24eb6d2f1227>__ as a gentle introduction to the main use cases, check the paper <https://dl.acm.org/doi/abs/10.1145/3487553.3524215>__ for more details on the relevant literature.

If you are not familiar with the library, we suggest first taking our small tour to get acquainted with the main abstractions through ready-made models and public datasets.

Quick Links

* Our `talk <https://www.youtube.com/watch?v=cAlJYxFYA04>`__ at Stitch Fix Algo Hour, for a general presentation.
* Our `paper <https://dl.acm.org/doi/abs/10.1145/3487553.3524215>`__, with in-depth analysis, detailed use cases and scholarly references (`arxiv <https://arxiv.org/abs/2111.09963>`__).
* A `colab notebook <https://colab.research.google.com/drive/1Wn5mm0csEkyWqmBBDxNBkfGR6CNfWeH-?usp=sharing>`__, showing how to train a cart recommender model from scratch and use the library to test it.
* Our `blog post <https://towardsdatascience.com/ndcg-is-not-all-you-need-24eb6d2f1227>`__, with examples and practical tips.


Supporters
~~~~~~~~
RecList is a community project made possible by the generous support of these awesome folks. Make sure to check them out!

Comet
=====

.. image:: https://github.com/jacopotagliabue/reclist/raw/main/images/comet.png
   :target: https://www.comet.com/?utm_source=jacopot&utm_medium=referral&utm_campaign=online_jacopot_2022&utm_content=github_reclist
   :width: 175

Neptune
=======

.. image:: https://github.com/jacopotagliabue/reclist/raw/main/images/neptune.png
   :target: https://neptune.ai
   :width: 175

Gantry
=======

.. image:: https://github.com/jacopotagliabue/reclist/raw/main/images/gantry.png
   :target: https://gantry.io/
   :width: 175


Project updates

Community Support: RecList is an open source community project made possible by the support of the awesome folks at Comet <https://www.comet.ml/site/>, Neptune <https://neptune.ai/> and Gantry <https://gantry.io/>__. Soon RecList tests will be natively integrated with the MLOps tools you already know and love!

June 2022: We launched a website <https://reclist.io/>__ to collect RecList materials, such as talks and presentations. RecList is powering the Data Challenge <https://reclist.io/cikm2022-cup/>__ at CIKM 2022: fill the form <https://docs.google.com/forms/d/e/1FAIpQLSfAypzM1mvd79JfRGRbb9QMfXGMoVYosdjU9C4NFEWNSNUZXQ/viewform>__ for updates.

In the last few months, we presented this library to practioners at Tubi, eBay, NVIDIA, BBC and other RecSys companies: we are in the process of collecting our thoughts after all the feedback we received, as we plan a beta release for this package in the next few months - come back often for updates, as we will also open a call for collaboration!

Please remember that the library is in alpha (i.e. enough working code to finish the paper and tinker with it). We welcome early feedback, but please be advised that the package may change substantially in the near future ("If you're not embarrassed by the first version, you've launched too late").

Summary

This doc is structured as follows:

* `Quick Start`_
* `A Guided Tour`_
* `Capabilities`_
* `Roadmap`_
* `Talks and Presentations`_
* `License and Citation`_
* `Acknowledgments`_

Quick Start
-----------

If you want to see *RecList* in action, clone the repository, create and activate a virtual env, and install the required packages from pip (you can install from root of course). If you prefer to experiment in an interactive, no-installation-required fashion, try out our `colab notebook <https://colab.research.google.com/drive/1Wn5mm0csEkyWqmBBDxNBkfGR6CNfWeH-?usp=sharing>`__.

Sample scripts are divided by use-cases: similar items, complementary items or session-based recommendations. When executing one, a suitable public dataset will be downloaded, and a baseline model trained: finally, the script will run a pre-made suite of behavioral tests to show typical results.

.. code-block:: bash

    git clone https://github.com/jacopotagliabue/reclist
    cd reclist
    python3 -m venv venv
    source venv/bin/activate
    pip install reclist
    python examples/coveo_complementary_rec.py

Running *your* model on one of the supported dataset, leveraging the pre-made tests, is as easy as implementing a simple interface, *RecModel*.

Once you've run successfully the sample script, take the guided tour below to learn more about the abstractions and the out-of-the-box capabilities of *RecList*.

A Guided Tour
-------------

An instance of `RecList <https://github.com/jacopotagliabue/reclist/blob/main/reclist/reclist.py>`__ represents a suite of tests for recommender systems: given
a dataset (more appropriately, an instance of `RecDataset <https://github.com/jacopotagliabue/reclist/blob/main/reclist/abstractions.py>`__)
and a model (an instance of `RecModel <https://github.com/jacopotagliabue/reclist/blob/main/reclist/abstractions.py>`__), it will run the specified tests on the target dataset, using the supplied model.

For example, the following code instantiates a pre-made suite of tests that contains sensible defaults for a `cart recommendation use case <https://github.com/jacopotagliabue/reclist/blob/main/reclist/reclist.py>`__:

.. code-block:: python

    rec_list = CoveoCartRecList(
        model=model,
        dataset=coveo_dataset
    )
    # invoke rec_list to run tests
    rec_list(verbose=True)

Our library pre-packages standard recSys KPIs and important behavioral tests, divided by use cases, but it is built with extensibility in mind: you can re-use tests in new suites, or you can write new domain-specific suites and tests.

Any suite must inherit the *RecList* interface, and then declare with Pytonic decorators its tests. In this case, the test re-uses a standard function:

.. code-block:: python

    class MyRecList(RecList):

        @rec_test(test_type='stats')
        def basic_stats(self):
            """
            Basic statistics on training, test and prediction data
            """
            from reclist.metrics.standard_metrics import statistics
            return statistics(self._x_train,
                self._y_train,
                self._x_test,
                self._y_test,
                self._y_preds)


Any model can be tested, as long as its predictions are wrapped in a *RecModel*. This allows for pure "black-box" testings,
a SaaS provider can be tested just by wrapping the proper API call in the method:

.. code-block:: python

    class MyCartModel(RecModel):

        def __init__(self, **kwargs):
            super().__init__(**kwargs)

        def predict(self, prediction_input: list, *args, **kwargs):
            """
            Implement the abstract method, accepting a list of lists, each list being
            the content of a cart: the predictions returned by the model are the top K
            items suggested to complete the cart.
            """

            return

More generally, the logical workflow of a typical RecList implementation is as follows (see our `blog post <https://towardsdatascience.com/ndcg-is-not-all-you-need-24eb6d2f1227>`__ for a longer explanation):

.. image:: https://github.com/jacopotagliabue/reclist/blob/main/images/workflow.gif
   :height: 400

While many standard KPIs are available in the package, the philosophy behind *RecList* is that metrics like Hit Rate provide only a partial picture
of the expected behavior of recommenders in the wild: two models with very similar accuracy can have very different behavior on, say, the long-tail, or
model A can be better than model B overall, but at the expense of providing disastrous performance on a set of inputs that are particularly important in production.

*RecList* recognizes that outside of academic benchmarks, some mistakes are worse than others, and not all inputs are created equal: when possible, it tries
to operationalize through scalable code behavioral insights for debugging and error analysis; it also
provides extensible abstractions when domain knowledge and custom logic are needed.

Once you run a suite of tests, results are dumped automatically and versioned in a local folder, structured as follows
(name of the suite, name of the model, run timestamp):

.. code-block::

    .reclist/
      myList/
        myModel/
          1637357392/
          1637357404/

If you start using *RecList* as part of your standard testings - either for research or production purposes - you can use the JSON report
for machine-to-machine communication with downstream system (e.g. you may want to automatically fail the model pipeline if certain behavioral tests are not passed).

Note: our app is deprecated, as RecList Beta will have connectors with existing apps (experiment trackers, model cards, etc.).

Capabilities
------------

*RecList* provides a dataset and model agnostic framework to scale up behavioral tests. As long as the proper abstractions
are implemented, all the out-of-the-box components can be re-used. For example:

* you can use a public dataset provided by *RecList* to train your new cart recommender model, and then use the *RecTests* we provide for that use case;

* you can use some baseline model on your custom dataset, to establish a baseline for your project;

* you can use a custom model, on a private dataset and define from scratch a new suite of tests, mixing existing methods and domain-specific tests.

We list below what we currently support out-of-the-box, with particular focus on datasets and tests, as the models we provide
are convenient baselines, but they are not meant to be SOTA research models.

Datasets

RecList features convenient wrappers around popular datasets, to help test models over known benchmarks in a standardized way.

• Coveo Data Challenge <https://github.com/coveooss/SIGIR-ecom-data-challenge>__
• (a smaller version of) The Million Playlist Dataset <https://engineering.atspotify.com/2018/05/30/introducing-the-million-playlist-dataset-and-recsys-challenge-2018/>__
• (a smaller version of) MovieLens <https://grouplens.org/datasets/movielens/>__

Behavioral Tests

RecList helps report standard quantitative metrics over popular (or custom) datasets, such as the ones collected in
*standard_metrics.py*: hit rate, mrr, coverage, popularity bias, etc. However, RecList raison d'etre is providing plug-and-play
behavioral tests, as agnostic as possible to the underlying models and datasets, while leaving open the possibility of writing
personalized tests when domain knowledge and custom logic are necessary.

Tests descriptions are available in our (WIP) `docs <https://reclist.readthedocs.io>`__, but we share here some examples from our `paper <https://dl.acm.org/doi/abs/10.1145/3487553.3524215>`__.

First, RecList allows to compare the performance of models which may have similar aggregate KPIs (e.g. hit rate on the entire
test set) in different slices. When plotting HR by product popularity, it is easy to spot that
prod2vec works much better with rarer items than the alternatives:

.. image:: https://github.com/jacopotagliabue/reclist/blob/main/images/hit_rate_dist.png
   :height: 175

When slicing by important meta-data (in this simulated example, brands), RecList uncovers significant differences
in performance for different groups; since the features we care about vary across datasets,
the package allows for a generic way to partition the test set and compute per-slice metrics:

.. image:: https://github.com/jacopotagliabue/reclist/blob/main/images/slice_dist.png
   :height: 175

Finally, RecList can take advantage of the latent item space to compute the cosine distances <query item, ground truth> and
<query item, prediction> for missed predictions in the test set. In a cart recommender use case, we expect items to
reflect the complementary nature of the suggestions: if a TV is in the cart, a model should recommend a HDMI cable,
not another TV. As we see in the comparison below, Google's predictions better match the label distribution,
suggesting that the model better capture the nature of the task:

.. image:: https://github.com/jacopotagliabue/reclist/blob/main/images/distance_to_query.png
   :height: 175

Roadmap
-------

We have exciting news about our Beta, including the usage of RecList as main library for the `CIKM Data Challenge <https://reclist.io/cikm2022-cup/>`__!

Contributing
~~~~~~~~~~~~

We will update this repo with some guidelines for contributions as soon as the codebase becomes more stable. Check back often for updates!

Acknowledgments
---------------

The original authors are:

* Patrick John Chia - `LinkedIn <https://www.linkedin.com/in/patrick-john-chia-b0a34019b/>`__, `GitHub <https://github.com/patrickjohncyh>`__
* Jacopo Tagliabue - `LinkedIn <https://www.linkedin.com/in/jacopotagliabue/>`__, `GitHub <https://github.com/jacopotagliabue>`__
* Federico Bianchi - `LinkedIn <https://www.linkedin.com/in/federico-bianchi-3b7998121/>`__, `GitHub <https://github.com/vinid>`__
* Chloe He - `LinkedIn <https://www.linkedin.com/in/chloe-he//>`__, `GitHub <https://github.com/chloeh13q>`__
* Brian Ko - `LinkedIn <https://www.linkedin.com/in/briankosw/>`__, `GitHub <https://github.com/briankosw>`__

If you have questions or feedback, please reach out to: :code:`jacopo dot tagliabue at tooso dot ai`.

Talks and Presentations
--------------------

Past and upcoming talks and presentations can be found at our new `website <https://reclist.io/>`__.

License and Citation
--------------------

All the code is released under an open MIT license. If you found *RecList* useful, please cite our WWW paper:

.. code-block:: bash

    @inproceedings{10.1145/3487553.3524215,
        author = {Chia, Patrick John and Tagliabue, Jacopo and Bianchi, Federico and He, Chloe and Ko, Brian},
        title = {Beyond NDCG: Behavioral Testing of Recommender Systems with RecList},
        year = {2022},
        isbn = {9781450391306},
        publisher = {Association for Computing Machinery},
        address = {New York, NY, USA},
        url = {https://doi.org/10.1145/3487553.3524215},
        doi = {10.1145/3487553.3524215},
        pages = {99–104},
        numpages = {6},
        keywords = {recommender systems, open source, behavioral testing},
        location = {Virtual Event, Lyon, France},
        series = {WWW '22 Companion}
    }

Credits
-------

This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage