163 //   => Nil  (there are three ratings fro this movie in ratings.csv but they are not positive)     

   163 //   => Nil  (there are three ratings for this movie in ratings.csv but they are not positive)     

   164 

   165 // (7) Calculate the recommendations for all movies according to

   166 // what the recommendations function in (6) produces (this

   167 // can take a few seconds). Put all recommendations into a list 

   168 // (of strings) and count how often the strings occur in

   169 // this list. This produces a list of string-int pairs,

   170 // where the first component is the movie name and the second

   171 // is the number of how many times they were recommended. 

   172 // Sort all the pairs according to the number

   173 // of times they were recommended (most recommended movie name 

   174 // first).

   175 

   176 def occurrences(xs: List[String]): List[(String, Int)] =

   177   for (x <- xs.distinct) yield (x, xs.count(_ == x))

   178 

   179 def most_recommended(recs: Map[String, List[String]],

   180                      movs: Map[String, String]) : List[(String, Int)] = {

   181    val all =  (for (name <- movs.toList.map(_._1)) yield {

   182      recommendations(recs, movs, name)                     

   183    }).flatten

   184    val occs = occurrences(all)

   185    occs.sortBy(_._2).reverse

   186 }

   166 // If you want to calculate the recomendations for all movies.

   189 //most_recommended(ratings_map, movies_map).take(3)

   167 // Will take a few seconds calculation time.

   190 // =>

   191 // List((Matrix,698), 

   192 //      (Star Wars: Episode IV - A New Hope (1977),402), 

   193 //      (Jerry Maguire (1996),382))