(mbodfbupvssftvmuttpgbs qsjoutpsujohtjn$boejebufttpsu@wbmvft joqmbdf svfbtdfoejoh 'bmtf qsjoutjn$boejebuftifbe for in range through the number of ratings that have in nz bujohti am going to add up similar movies to the ones that rated soi' going to take that dpss busjy dataframethat magical one that has all of the movie similaritiesand am going to create correlation matrix with nz bujohtdrop any missing valuesand then am going to scale that resulting correlation score by how well rated that movie sothe idea here is ' going to go through all the similarities for the empire strikes backfor exampleand will scale it all by because really liked the empire strikes back butwhen go through and get the similarities for gone with the windi' only going to scale those by because did not like gone with the wind sothis will give more strength to movies that are similar to movies that likedand less strength to movies that are similar to movies that did not likeokaysoi just go through and build up this list of similarity candidatesrecommendation candidates if you willsort the results and print them out let' see what we getheythose don' look too badrightsoobviously if&nqjsf usjlft#bdl and ubs bst come out on topbecause like those movies explicitlyi already watched them and rated them butbubbling up to the top of the list is fuvsopguif +fej which we would expect and bjefstpguif-ptu"sl 