// Core Part about Movie Recommendations // at Danube.co.uk//===========================================object CW7b {import io.Sourceimport scala.util._// (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 ratings_url = """https://nms.kcl.ac.uk/christian.urban/ratings.csv"""val movies_url = """https://nms.kcl.ac.uk/christian.urban/movies.csv"""// testcases//-----------//://val movies = get_csv_url(movies_url)//ratings.length // 87313//movies.length // 9742// (2) Implement two functions that process the CSV-files from (1). 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. Note the input to these functions, that is// the argument lines, will be the output of the function get_csv_url.def process_ratings(lines: List[String]) : List[(String, String)] = ???def process_movies(lines: List[String]) : List[(String, String)] = ???// testcases//-----------//val good_ratings = process_ratings(ratings)//val movie_names = process_movies(movies)//good_ratings.length //48580//movie_names.length // 9742// (3) Implement a grouping function that calculates 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 m// is set to Map() at the beginning of the calculation.def groupById(ratings: List[(String, String)], m: Map[String, List[String]]) : Map[String, List[String]] = ???// testcases//-----------//val ratings_map = groupById(good_ratings, Map())//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 in its recommendations. It ReTurns a list of all these// recommendations (each of them is a list and needs to have the movie deleted, // otherwise it might happen we recommend the same movie).def favourites(m: Map[String, List[String]], mov: String) : List[List[String]] = ???// testcases//-----------// 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.def suggestions(recs: Map[String, List[String]], mov_name: String) : List[String] = ???// testcases//-----------//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 a recommendations function 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] = ???// 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 for this movie in ratings.csv but they are not positive) // (7) Calculate the recommendations for all movies according to// what the recommendations function in (6) produces (this// can take a few seconds). Put all recommendations into a list // (of strings) and count how often the strings occur in// this list. This produces a list of string-int pairs,// where the first component is the movie name and the second// is the number of how many times the movie was recommended. // Sort all the pairs according to the number// of times they were recommended (most recommended movie name // first).def most_recommended(recs: Map[String, List[String]], movs: Map[String, String]) : List[(String, Int)] = ???// testcase////most_recommended(ratings_map, movies_map).take(3)// =>// List((Matrix,698), // (Star Wars: Episode IV - A New Hope (1977),402), // (Jerry Maguire (1996),382))}