.. image:: pyeco.png
    :height: 20
    :alt: Economie
    :target: http://www.xavierdupre.fr/app/ensae_teaching_cs/helpsphinx/td_2a_notions.html#pour-un-profil-plutot-economiste

.. image:: pystat.png
    :height: 20
    :alt: Statistique
    :target: http://www.xavierdupre.fr/app/ensae_teaching_cs/helpsphinx/td_2a_notions.html#pour-un-profil-plutot-data-scientist

.. _l-td2a-sys-recommandation:

Système de recommandations
++++++++++++++++++++++++++

*(à venir)*

*Lectures*

* `Item-Based Collaborative Filtering Recommendation Algorithms <http://files.grouplens.org/papers/www10_sarwar.pdf>`_
* `Recommendations in Keras using triplet loss <https://github.com/maciejkula/triplet_recommendations_keras>`_
* `AutoRec: Autoencoders Meet Collaborative Filtering <http://users.cecs.anu.edu.au/~akmenon/papers/autorec/autorec-paper.pdf>`_,
  `Hybrid Recommender System based on Autoencoders <https://hal.inria.fr/hal-01336912/file/AutoEnc.pdf>`_
* `ACP et factorisation de matrices <http://www.xavierdupre.fr/app/mlstatpy/helpsphinx/c_ml/missing_values_mf.html>`_
* `The Why and How of Nonnegative Matrix Factorization <https://arxiv.org/abs/1401.5226>`_
* `A tutorial on Non-Negative Matrix Factorisation with Applications to Audiovisual Content Analysis <http://perso.telecom-paristech.fr/~essid/teach/NMF_tutorial_ICME-2014.pdf>`_
* `Large-Scale Matrix Factorization with Missing Data under Additional Constraints <http://www.cfar.umd.edu/~rama/Publications/mitra_nips_2010.pdf>`_
* `Quick Guide to Build a Recommendation Engine in Python <https://www.analyticsvidhya.com/blog/2016/06/quick-guide-build-recommendation-engine-python/>`_
* `Recommender Systems in Python: Beginner Tutorial <https://www.datacamp.com/community/tutorials/recommender-systems-python>`_
* `Recommender Systems Using Linear Classifiers <http://www.jmlr.org/papers/volume2/zhang02a/zhang02a.pdf>`_ (approche intéressante)
* `A Survey of Accuracy Evaluation Metrics of Recommendation Tasks <http://jmlr.csail.mit.edu/papers/volume10/gunawardana09a/gunawardana09a.pdf>`_
* `Offline A/B Testing for Recommender Systems <https://dl.acm.org/citation.cfm?id=3159687>`_
* `Probabilistic Matrix Factorization <http://papers.nips.cc/paper/3208-probabilistic-matrix-factorization.pdf>`_
* `A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems <https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/libmf_journal.pdf>`_
* `Smart Adaptive Recommendations (SAR) Algorithm <https://github.com/Microsoft/Product-Recommendations/blob/master/doc/sar.md>`_
* `Flexible, Scalable, Differentiable Simulation of Recommender Systems with RecSim NG
  <https://ai.googleblog.com/2021/04/flexible-scalable-differentiable.html?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+blogspot%2FgJZg+%28Google+AI+Blog%29>`_
* `A quantum-inspired classical algorithm for recommendation systems
  <https://arxiv.org/abs/1807.04271>`_

*Modules*

* `scikit-learn <http://scikit-learn.org/stable/>`_
* `NonnegMFPy <https://github.com/guangtunbenzhu/NonnegMFPy>`_ : implémentation de
  l'algorithme décrit dans l'article
  `Large-Scale Matrix Factorization with Missing Data under Additional Constraints <http://www.cfar.umd.edu/~rama/Publications/mitra_nips_2010.pdf>`_
* `scikit-surprise <http://surpriselib.com/>`_ (`documentation <http://surprise.readthedocs.io/en/stable/>`_)
* `pyrwr <https://github.com/jinhongjung/pyrwr>`_