Recommendation Engines:
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Neighborhood Based Collaborative Filtering: common ways to measure the similarity between two users (or two items) including:
    * `Measures of Similarity <https://dataaspirant.com/five-most-popular-similarity-measures-implementation-in-python/>`_
    * Pearson's correlation coefficient
    * Spearman's correlation coefficient
    * Kendall's Tau
    * Euclidean Distance
    * Manhattan Distance

Recommendation System: 
    * `Collaborative Filtering <https://blog.dominodatalab.com/recommender-systems-collaborative-filtering/>`_
    * `Item Weighting in Collaborative <https://www.semanticscholar.org/paper/Item-Weighting-Techniques-for-Collaborative-Baltrunas-Ricci/3e9ebcd9503ef7375c7bb334511804d1e45127e9?p2df>`_

Techniques invovled :
    1. `Dimensionality reduction <http://infolab.stanford.edu/~ullman/mmds/ch11.pdf>`_
        * `Singular value Decomposition <http://web.mit.edu/be.400/www/SVD/Singular_Value_Decomposition.htm>`_
            #. `SVD non-negative propery <https://math.stackexchange.com/questions/2060572/why-are-singular-values-always-non-negative>`_
            #. `python SVD from scratch <https://machinelearningmastery.com/singular-value-decomposition-for-machine-learning/>`_
            #. `Missing values SVD <https://www.hindawi.com/journals/mpe/2015/380472/>`_


Articles to read : 
    * `Evaluation recommender systems <https://medium.com/recombee-blog/evaluating-recommender-systems-choosing-the-best-one-for-your-business-c688ab781a35>`_
    * `AirBnB recommendation <https://medium.com/airbnb-engineering/listing-embeddings-for-similar-listing-recommendations-and-real-time-personalization-in-search-601172f7603e>`_
    * `Location based recommendation systems <https://link.springer.com/referenceworkentry/10.1007%2F978-3-319-17885-1_1580>`_
    * `Deep learning <https://ebaytech.berlin/deep-learning-for-recommender-systems-48c786a20e1a>`_
    * `Objective setting of recommendation systems <https://gab41.lab41.org/recommender-systems-its-not-all-about-the-accuracy-562c7dceeaff>`_
    * `Scales for response collection <https://cxl.com/blog/survey-response-scales/>`_
    * `SVD system introduction <https://towardsdatascience.com/an-introduction-to-svd-and-its-widely-used-applications-f5b8f19cb6cb>`_
    * `Funk SVD working <https://medium.com/datadriveninvestor/how-funk-singular-value-decomposition-algorithm-work-in-recommendation-engines-36f2fbf62cac>`_


Handling Issues : 
    * `Cold User Problem <https://towardsdatascience.com/solving-cold-user-problem-for-recommendation-system-using-multi-armed-bandit-d36e42fe8d44>`_
    * `Comparison of user vs item collaborative filtering <https://medium.com/@wwwbbb8510/comparison-of-user-based-and-item-based-collaborative-filtering-f58a1c8a3f1d>`_
    * `Recommendation using matrix Factorization <https://medium.com/@paritosh_30025/recommendation-using-matrix-factorization-5223a8ee1f4>`_