public async Task <IEnumerable <GeneralRating> > GetMappedMovies(IEnumerable <UserImdbRating> ratingList)
{
try
{
List <GeneralRating> mappedRatings = new List <GeneralRating>();
foreach (UserImdbRating imdbRating in ratingList)
{
MovieMap movie = await _context.Movies
.Find(movie => movie.Title.ToLower().Contains(imdbRating.MovieTitle.ToLower()))
.FirstOrDefaultAsync();
if (movie != null)
{
GeneralRating mappedRating = new GeneralRating
{
MovieId = movie.MovieId,
RatingValue = imdbRating.RatingValue / 2
};
mappedRatings.Add(mappedRating);
}
}
return(mappedRatings);
}
catch (Exception ex)
{
throw ex;
}
}
public static List <GeneralRating> ConvertRatings(List <Rating> ratings)
{
List <GeneralRating> ListOfRatings = new List <GeneralRating>();
for (int i = 0; i < ratings.Count; i++)
{
GeneralRating gRating = new GeneralRating(ratings[i].UserId, ratings[i].BookId, ratings[i].Rating1);
ListOfRatings.Add(gRating);
}
return(ListOfRatings);
}
/// <summary>
/// Заполняет матрицу рейтингами элементов, которые оценили оба пользователя
/// </summary>
/// <param name="userA"></param>
/// <param name="userB"></param>
private void calculate(GeneralUser userA, GeneralUser userB)
{
userA.getAllRatings().ForEach(delegate(GeneralRating r)
{
GeneralRating r2 = userB.getItemRating(r.getItemId());
if (r2 != null)
{
int i = r.getRating() - 1;
int j = r2.getRating() - 1;
matrix[i, j]++;
}
});
}
/// <summary>
/// Заполняет матрицу, используя юзер рейтинги для соответствующих элементов.
/// Рассматриваются юзеры, которые оценили оба элемента
/// </summary>
/// <param name="itemA"></param>
/// <param name="itemB"></param>
private void calculate(GeneralItem itemA, GeneralItem itemB)
{
itemA.getAllRatings().ForEach(delegate(GeneralRating r)
{
GeneralRating r2 = itemB.getUserRating(r.getUserId());
if (r2 != null)
{
int i = r.getRating() - 1;
int j = r2.getRating() - 1;
matrix[i, j]++;
}
});
}
private double estimateItemBasedRating(BookUser user, BookItem item)
{
double estimatedRating = item.getAverageRating();
int itemId = item.getId();
int userId = user.UserId;
double similaritySum = 0.0;
double weightedRatingSum = 0.0;
GeneralRating existingRatingByUser = user.getItemRating(item.getId());
if (existingRatingByUser != null)
{
estimatedRating = existingRatingByUser.getRating();
}
else
{
double similarityBetweenItems = 0;
double weightedRating = 0;
dataSet.getBooks().ForEach(delegate(BookItem anotherItem) {
// Рассматриваются только те элементы, которые оценивал пользователь
GeneralRating anotherItemRating = anotherItem.getUserRating(userId);
if (anotherItemRating != null)
{
similarityBetweenItems = similarityMatrix.getValue(itemId, anotherItem.getId());
if (similarityBetweenItems > similarityThreshold)
{
weightedRating = similarityBetweenItems * anotherItemRating.getRating();
weightedRatingSum += weightedRating;
similaritySum += similarityBetweenItems;
}
}
});
if (similaritySum > 0.0)
{
estimatedRating = weightedRatingSum / similaritySum;
}
}
return(estimatedRating);
}
//--------------------------------------------------------------------
// Приватные вспомогательные методы
//--------------------------------------------------------------------
private double estimateUserBasedRating(BookUser user, BookItem item)
{
double estimatedRating = user.getAverageRating();
int itemId = item.getId();
int userId = user.UserId;
double similaritySum = 0.0;
double weightedRatingSum = 0.0;
// Проверка, оценивал ли пользователь этот элемент
GeneralRating existingRatingByUser = user.getItemRating(item.getId());
if (existingRatingByUser != null)
{
estimatedRating = existingRatingByUser.getRating();
}
else
{
dataSet.getUsers().ForEach(delegate(BookUser anotherUser){
GeneralRating itemRating = anotherUser.getItemRating(itemId);
if (itemRating != null)
{
double similarityBetweenUsers = similarityMatrix.getValue(userId, anotherUser.UserId);
double ratingByNeighbor = itemRating.getRating();
double weightedRating = similarityBetweenUsers * ratingByNeighbor;
weightedRatingSum += weightedRating;
similaritySum += similarityBetweenUsers;
}
});
if (similaritySum > 0.0)
{
estimatedRating = weightedRatingSum / similaritySum;
}
}
return(estimatedRating);
}
