static void Main(string[] args)
{
KMeans km = new KMeans();
int nItemsCount = km.LoadItemsFromFile(FileNames.MoviesFile);
Dictionary <string, List <string> > usersAndTheirSeenMovies = new Dictionary <string, List <string> >();
usersAndTheirSeenMovies = FileNames.LoadUserDataFromFile(FileNames.UsersMovies);
var user = ReadUserForRecommendation(usersAndTheirSeenMovies);
var seenMovies = usersAndTheirSeenMovies[user];
IClustersList obtainedClusters = new IClustersList();
obtainedClusters = ComputeClustering(km, FileNames.GenresFile, nItemsCount, "Enter the number of movies per genre");
File.Delete(FileNames.RecommendedMovies);
RecommendMoviesBasedOnClusters(seenMovies, obtainedClusters);
km = new KMeans();
nItemsCount = km.LoadItemsFromFile(FileNames.RecommendedMovies);
var newObtainedClusters = ComputeClustering(km, FileNames.RankingFile, nItemsCount, "Enter the number of movies per rank");
RecommendMoviesBasedOnClusters(seenMovies, newObtainedClusters, true);
Console.Read();
}
public object Clone()
{
IClustersList TargetClustersList = new IClustersList();
foreach (ICluster cluster in ClustersList)
{
IItemsList TargetCentroidsList = new IItemsList();
foreach (Item centroid in (IItemsList)cluster.Centroids.Clone())
{
TargetCentroidsList.Add(new Item(centroid.ItemText, (IAttributeList)centroid.AttributeList.Clone(),
centroid.Distance, centroid.IsCentroid, centroid.Exists));
}
IItemsList TargetItemsList = new IItemsList();
foreach (Item item in (IItemsList)cluster.Items.Clone())
{
TargetItemsList.Add(new Item(item.ItemText, (IAttributeList)item.AttributeList.Clone(),
item.Distance, item.IsCentroid, item.Exists));
}
TargetClustersList.Add(new ICluster((IItemsList)TargetCentroidsList.Clone(),
(IItemsList)TargetItemsList.Clone()));
}
return(TargetClustersList);
}
private static void RecommendMoviesBasedOnClusters(List <string> seenMovies, IClustersList obtainedClusters, bool isRankingBased = false)
{
Dictionary <int, List <KeyValuePair <ICluster, List <string> > > > ocurrencesAndClustersMapping = new Dictionary <int, List <KeyValuePair <ICluster, List <string> > > >();
int sum = 0;
foreach (ICluster cluster in obtainedClusters.ClustersList)
{
int countPerCluster = 0;
List <string> seenMovieItems = new List <string>();
foreach (var item in cluster.Items.ItemsList)
{
foreach (string movieName in seenMovies)
{
if (item.ItemText.Contains(movieName))
{
countPerCluster += 1;
seenMovieItems.Add(item.ItemText);
sum += Int32.Parse(item.ItemText.Split(' ').LastOrDefault());
}
}
}
if (ocurrencesAndClustersMapping.ContainsKey(countPerCluster))
{
ocurrencesAndClustersMapping[countPerCluster].Add(new KeyValuePair <ICluster, List <string> >(cluster, seenMovieItems));
}
else
{
ocurrencesAndClustersMapping.Add(countPerCluster, new List <KeyValuePair <ICluster, List <string> > > {
new KeyValuePair <ICluster, List <string> >(cluster, seenMovieItems)
});
}
}
if (!isRankingBased)
{
Average = sum / seenMovies.Count;
}
var maxKey = ocurrencesAndClustersMapping.Keys.Max();
var chosenClusterData = ocurrencesAndClustersMapping[maxKey];
var recommendations = new List <string>();
foreach (var data in chosenClusterData)
{
foreach (var item in data.Key.Items.ItemsList)
{
if (!data.Value.Contains(item.ItemText))
{
if (isRankingBased)
{
if (Int32.Parse(item.ItemText.Split().LastOrDefault()) >= Average)
{
recommendations.Add(item.ItemText);
}
}
else
{
recommendations.Add(item.ItemText);
File.AppendAllLines(FileNames.RecommendedMovies, new List <string> {
item.ItemText
});
}
}
}
}
Console.WriteLine("Future recommendations");
Console.WriteLine("_____________________");
foreach (var recomm in recommendations)
{
Console.WriteLine(recomm);
}
}
