// Core Part about Movie Recommendations + −
// at Danube.co.uk+ −
//===========================================+ −
+ −
object CW7b {+ −
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import io.Source+ −
import scala.util._+ −
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// (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] = ???+ −
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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+ −
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// (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.+ −
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def process_ratings(lines: List[String]) : List[(String, String)] = ???+ −
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def process_movies(lines: List[String]) : List[(String, String)] = ???+ −
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// testcases+ −
//-----------+ −
//val good_ratings = process_ratings(ratings)+ −
//val movie_names = process_movies(movies)+ −
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//good_ratings.length //48580+ −
//movie_names.length // 9742+ −
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// (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]] = ???+ −
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// 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+ −
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//ratings_map.get("214").get.map(movies_map.get(_)) + −
// => least prolific recommender with only 1 positive rating+ −
+ −
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// (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]] = ???+ −
+ −
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// testcases+ −
//-----------+ −
// movie ID "912" -> Casablanca (1942)+ −
// "858" -> Godfather+ −
// "260" -> Star Wars: Episode IV - A New Hope (1977)+ −
+ −
//favourites(ratings_map, "912").length // => 80+ −
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// 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.+ −
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// (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+ −
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// (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.+ −
+ −
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def recommendations(recs: Map[String, List[String]],+ −
movs: Map[String, String],+ −
mov_name: String) : List[String] = ???+ −
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// testcases+ −
//-----------+ −
// recommendations(ratings_map, movies_map, "912")+ −
// => List(Godfather, Star Wars: Episode IV - A NewHope (1977))+ −
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//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) + −
+ −
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// (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)] = ???+ −
+ −
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// 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))+ −
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