understanding movie recommendations with an example sowhat we do with this datawellwhat we want to do is recommend movies for people the way we do that is we look at all the ratings for given personfind movies similar to the stuff that they ratedand those are candidates for recommendations to that person let' start by creating fake person to create recommendations for 've actually already added fake user by handid number to the movielens dataset that we're processing you can see that user with the following codenz bujoht vtfs bujohtmpdespqob nz bujoht this gives the following outputthat kind of represents someone like mewho loved star wars and the empire strikes backbut hated the movie gone with the wind sothis represents someone who really loves star warsbut does not like old styleromantic dramasokaysoi gave rating of star to if&nqjsf usjlft#bdl and ubs bst and rating of star to (pofxjuiuif joe soi' going to try to find recommendations for this fictitious user sohow do do thatwelllet' start by creating series called tjn$boejebuft and ' going to go through every movie that rated tjn$boejebuft qe fsjft gpsjjosbohf mfo nz bujohtjoefy qsjou"eejohtjntgpsnz bujohtjoefy  fusjfwftjnjmbsnpwjftupuijtpofuibu*sbufe tjnt dpss busjy>espqob /pxtdbmfjuttjnjmbsjuzczipxxfmm*sbufeuijtnpwjf tjnt tjntnbq mbnceby nz bujoht "eeuiftdpsfupuifmjtupgtjnjmbsjuzdboejebuft tjn$boejebuft tjn$boejebuftbqqfoe tjnt