public static void main(String[] args)
{
DataModel model = new FileDataModel(new File(args[0]));
int howMany = 10;
if (args.Length > 1)
{
howMany = Integer.parseInt(args[1]);
}
System.out.println("Run Items");
ItemSimilarity similarity = new EuclideanDistanceSimilarity(model);
Recommender recommender = new GenericItemBasedRecommender(model, similarity); // Use an item-item recommender
for (int i = 0; i < LOOPS; i++)
{
LoadStatistics loadStats = LoadEvaluator.runLoad(recommender, howMany);
System.out.println(loadStats);
}
System.out.println("Run Users");
UserSimilarity userSim = new EuclideanDistanceSimilarity(model);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(10, userSim, model);
recommender = new GenericUserBasedRecommender(model, neighborhood, userSim);
for (int i = 0; i < LOOPS; i++)
{
LoadStatistics loadStats = LoadEvaluator.runLoad(recommender, howMany);
System.out.println(loadStats);
}
}
public static void main(String[] args)
{
DataModel model = new FileDataModel(new File(args[0]));
int howMany = 10;
if (args.Length > 1) {
howMany = Integer.parseInt(args[1]);
}
System.out.println("Run Items");
ItemSimilarity similarity = new EuclideanDistanceSimilarity(model);
Recommender recommender = new GenericItemBasedRecommender(model, similarity); // Use an item-item recommender
for (int i = 0; i < LOOPS; i++) {
LoadStatistics loadStats = LoadEvaluator.runLoad(recommender, howMany);
System.out.println(loadStats);
}
System.out.println("Run Users");
UserSimilarity userSim = new EuclideanDistanceSimilarity(model);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(10, userSim, model);
recommender = new GenericUserBasedRecommender(model, neighborhood, userSim);
for (int i = 0; i < LOOPS; i++) {
LoadStatistics loadStats = LoadEvaluator.runLoad(recommender, howMany);
System.out.println(loadStats);
}
}
public IRecommender BuildRecommender(IDataModel model)
{
IUserSimilarity similarity = new EuclideanDistanceSimilarity(model);
IUserNeighborhood neighborhood =
new NearestNUserNeighborhood(10, similarity, model);
return
(new GenericUserBasedRecommender(model, neighborhood, similarity));
}
public long[] GetNNearestNeighborsUsersRecommendations(int numNeighbours, int userId)
{
GenericDataModel model = GetUserBasedDataModel();
EuclideanDistanceSimilarity similarity = new EuclideanDistanceSimilarity(
model);
IUserNeighborhood neighborhood = new NearestNUserNeighborhood(
20, similarity, model);
long[] neighbors = neighborhood.GetUserNeighborhood(userId);
var recommender =
new GenericUserBasedRecommender(model, neighborhood, similarity);
var recommendedItems = recommender.Recommend(userId, 8);
return(neighbors);
}
public List <int> GetRecommendations(int userId)
{
GenericDataModel model = GetUserBasedDataModel();
EuclideanDistanceSimilarity similarity = new EuclideanDistanceSimilarity(
model);
IUserNeighborhood neighborhood = new NearestNUserNeighborhood(
15, similarity, model);
long[] neighbors = neighborhood.GetUserNeighborhood(userId);
var recommender =
new GenericUserBasedRecommender(model, neighborhood, similarity);
var recommendedItems = recommender.Recommend(userId, 12);
var bookIds = recommendedItems.Select(ri => (int)ri.GetItemID()).ToList();
return(bookIds);
}
