// Core Part about Movie Recommendations + −
// at Danube.co.uk+ −
//===========================================+ −
+ −
import io.Source+ −
import scala.util._+ −
+ −
object CW7b { // for purposes of generating a jar+ −
+ −
// (1) Implement the function get_csv_url which takes an url-string+ −
// as argument and requests the corresponding file. The two urls+ −
// of interest are ratings_url and movies_url, which correspond + −
// to CSV-files.+ −
// The function should ReTurn the CSV file appropriately broken+ −
// up into lines, and the first line should be dropped (that is without+ −
// the header of the CSV file). The result is a list of strings (lines+ −
// in the file).+ −
+ −
def get_csv_url(url: String) : List[String] = {+ −
val csv = Source.fromURL(url)("ISO-8859-1")+ −
csv.mkString.split("\n").toList.drop(1)+ −
}+ −
+ −
val ratings_url = """https://nms.kcl.ac.uk/christian.urban/ratings.csv"""+ −
val movies_url = """https://nms.kcl.ac.uk/christian.urban/movies.csv"""+ −
+ −
// test cases+ −
+ −
//val ratings = get_csv_url(ratings_url)+ −
//val movies = get_csv_url(movies_url)+ −
+ −
//ratings.length // 87313+ −
//movies.length // 9742+ −
+ −
// (2) Implement two functions that process the CSV files. The ratings+ −
// function filters out all ratings below 4 and ReTurns a list of + −
// (userID, movieID) pairs. The movies function just ReTurns a list + −
// of (movieId, title) pairs.+ −
+ −
+ −
def process_ratings(lines: List[String]) : List[(String, String)] = {+ −
for (cols <- lines.map(_.split(",").toList); + −
if (cols(2).toFloat >= 4)) yield (cols(0), cols(1)) + −
}+ −
+ −
def process_movies(lines: List[String]) : List[(String, String)] = {+ −
for (cols <- lines.map(_.split(",").toList)) yield (cols(0), cols(1)) + −
}+ −
+ −
// test cases+ −
+ −
//val good_ratings = process_ratings(ratings)+ −
//val movie_names = process_movies(movies)+ −
+ −
//good_ratings.length //48580+ −
//movie_names.length // 9742+ −
+ −
//==============================================+ −
// Do not change anything below, unless you want + −
// to submit the file for the advanced part 3!+ −
//==============================================+ −
+ −
+ −
// (3) Implement a grouping function that calulates a map+ −
// containing the userIds and all the corresponding recommendations + −
// (list of movieIds). This should be implemented in a tail+ −
// recursive fashion, using a map m as accumulator. This map+ −
// is set to Map() at the beginning of the claculation.+ −
+ −
def groupById(ratings: List[(String, String)], + −
m: Map[String, List[String]]) : Map[String, List[String]] = ratings match {+ −
case Nil => m+ −
case (id, mov) :: rest => {+ −
val old_ratings = m.getOrElse (id, Nil)+ −
val new_ratings = m + (id -> (mov :: old_ratings))+ −
groupById(rest, new_ratings)+ −
}+ −
}+ −
+ −
//+ −
//val ls = List(("1", "a"), ("2", "a"), ("1", "c"), ("2", "a"), ("1", "c"))+ −
//+ −
//val m = groupById(ls, Map())+ −
//+ −
//m.getOrElse("1", Nil).count(_ == "c") // => 2+ −
//m.getOrElse("1", Nil).count(_ == "a") // => 1+ −
+ −
// test cases+ −
//val ratings_map = groupById(good_ratings, Map())+ −
//groupById(good_ratings, Map()).get("214")+ −
//groupById(good_ratings, Map()).toList.minBy(_._2.length)+ −
//val movies_map = movie_names.toMap+ −
+ −
//ratings_map.get("414").get.map(movies_map.get(_)) // most prolific recommender with 1227 positive ratings+ −
//ratings_map.get("474").get.map(movies_map.get(_)) // second-most prolific recommender with 787 positive ratings+ −
//ratings_map.get("214").get.map(movies_map.get(_)) // least prolific recommender with only 1 positive rating+ −
+ −
+ −
+ −
//(4) Implement a function that takes a ratings map and a movie_name as argument.+ −
// The function calculates all suggestions containing+ −
// the movie mov in its recommendations. It ReTurns a list of all these+ −
// recommendations (each of them is a list and needs to have mov deleted, + −
// otherwise it might happen we recommend the same movie).+ −
+ −
def favourites(m: Map[String, List[String]], mov: String) : List[List[String]] = + −
(for (id <- m.keys.toList;+ −
if m(id).contains(mov)) yield m(id).filter(_ != mov))+ −
+ −
+ −
+ −
// test cases+ −
// movie ID "912" -> Casablanca (1942)+ −
// "858" -> Godfather+ −
// "260" -> Star Wars: Episode IV - A New Hope (1977)+ −
+ −
//favourites(ratings_map, "912").length // => 80+ −
+ −
// That means there are 80 users that recommend the movie with ID 912.+ −
// Of these 80 users, 55 gave a good rating to movie 858 and+ −
// 52 a good rating to movies 260, 318, 593.+ −
+ −
+ −
// (5) Implement a suggestions function which takes a rating+ −
// map and a movie_name as arguments. It calculates all the recommended+ −
// movies sorted according to the most frequently suggested movie(s) first.+ −
+ −
// needed in Scala 2.13.+ −
+ −
def mapValues[S, T, R](m: Map[S, T], f: T => R) =+ −
m.map { case (x, y) => (x, f(y)) }+ −
+ −
def suggestions(recs: Map[String, List[String]], + −
mov_name: String) : List[String] = {+ −
val favs = favourites(recs, mov_name).flatten+ −
val favs_counted = mapValues(favs.groupBy(identity), (v:List[String]) => v.size).toList+ −
val favs_sorted = favs_counted.sortBy(_._2).reverse+ −
favs_sorted.map(_._1)+ −
}+ −
+ −
// check+ −
// groupMap is equivalent to groupBy(key).mapValues(_.map(f))+ −
+ −
// test cases+ −
+ −
//suggestions(ratings_map, "912")+ −
//suggestions(ratings_map, "912").length + −
// => 4110 suggestions with List(858, 260, 318, 593, ...)+ −
// being the most frequently suggested movies+ −
+ −
// (6) Implement recommendations functions which generates at most+ −
// *two* of the most frequently suggested movies. It Returns the + −
// actual movie names, not the movieIDs.+ −
+ −
def recommendations(recs: Map[String, List[String]],+ −
movs: Map[String, String],+ −
mov_name: String) : List[String] =+ −
suggestions(recs, mov_name).take(2).map(movs.get(_).get) + −
+ −
+ −
// testcases+ −
+ −
// recommendations(ratings_map, movies_map, "912")+ −
// => List(Godfather, Star Wars: Episode IV - A NewHope (1977))+ −
+ −
//recommendations(ratings_map, movies_map, "260")+ −
// => List(Star Wars: Episode V - The Empire Strikes Back (1980), + −
// Star Wars: Episode VI - Return of the Jedi (1983))+ −
+ −
// recommendations(ratings_map, movies_map, "2")+ −
// => List(Lion King, Jurassic Park (1993))+ −
+ −
// recommendations(ratings_map, movies_map, "0")+ −
// => Nil+ −
+ −
// recommendations(ratings_map, movies_map, "1")+ −
// => List(Shawshank Redemption, Forrest Gump (1994))+ −
+ −
// recommendations(ratings_map, movies_map, "4")+ −
// => Nil (there are three ratings fro this movie in ratings.csv but they are not positive) + −
+ −
+ −
// If you want to calculate the recomendations for all movies.+ −
// Will take a few seconds calculation time.+ −
+ −
//val all = for (name <- movie_names.map(_._1)) yield {+ −
// recommendations(ratings_map, movies_map, name)+ −
//}+ −
+ −
// helper functions+ −
//List().take(2)+ −
//List(1).take(2)+ −
//List(1,2).take(2)+ −
//List(1,2,3).take(2)+ −
+ −
}+ −