figure - recommendation by the item similarity based recommendation engine ##note at the start of this sectionwe mentioned we don' readily have access to song' features that we can use to define similarity as part of the million song databasewe have those features available for each of the songs in the dataset you are encouraged to replace this implicit similaritybased on common userswith the explicit similarity based on features of the song and see how the recommendations change matrix factorization based recommendation engine matrix factorization based recommendation engines are probably the most used recommendation engines when it comes to implementing recommendation engines in production in this sectionwe give an intuition-based introduction to matrix factorization based recommendation engines we avoid going into heavily mathematical discussionas from practitioner' perspective the intent is to see how we can leverage this to get valuable recommendations from real data matrix factorization refers to identification of two or more matrices from an initial matrixsuch that when these matrices are multiplied we get the original matrix matrix factorization can be used to discover latent features between two different kinds of entities what are these latent featureslet' discuss that for moment before we go for mathematical explanation consider for moment why you like certain song--the answer may range from the soulful lyricsto catchy musicto it being melodiousand many more we can try to explain song in mathematical terms by measuring its beatstempoand other such features and then define similar features in terms of the user for examplewe can define that from user' listening history we know that he likes songs with beats that are bit on the higher sideetc once we have consciously defined such "features,we can use them to find matches for user based on some similarity criteria but more often than notthe tough part in this process is defining these featuresas we have no handbook for what will make good feature it is mostly based on domain experts and bit of experimentation but as you will see in this sectionyou can use matrix factorization to discover these latent features and they seem to work great