public IList <Book> Recommend(Preference preference, int limit)
{
IList <Book> books = new List <Book>(limit);
DataLoaderFactory dataLoaderFactory = DataLoaderFactory.getInstance();
IDataLoader loader = dataLoaderFactory.GetLoader();
IRecommender recommender = new PearsonRecommender();
IRatingsAggregator aggregator = new RatingsAggregator();
BookDetail bookDetail = loader.Load();
Dictionary <string, List <int> > bookRatings = aggregator.Aggregate(bookDetail, preference);
List <int> Base = bookRatings[preference.ISBN];
bookRatings.Remove(preference.ISBN);
Dictionary <string, double> coefficients = new Dictionary <string, double>();
foreach (var item in bookRatings)
{
coefficients.Add(item.Key, recommender.GetCorrelation(Base, bookRatings[item.Key]));
}
coefficients = coefficients.OrderByDescending(x => x.Value).ToDictionary(a => a.Key, b => b.Value);
coefficients = coefficients.Take(limit).ToDictionary(a => a.Key, b => b.Value);
books.Add(bookDetail.Books.Find(b => b.ISBN == preference.ISBN));
foreach (var isbn in coefficients.Keys)
{
books.Add(bookDetail.Books.Find(b => b.ISBN == isbn));
}
return(books);
}
public void GetCorrelation_ForValidInput_GetsValidResult()
{
List <int> baseArray = new List <int> {
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
};
List <int> dataArray = new List <int> {
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
};
double actual;
IRecommender target = new PearsonRecommender();
actual = target.GetCorrelation(baseArray, dataArray);
Assert.AreEqual(actual, 1);
}
public List <Book> Recommend(Preference preference, int limit)
{
BooksDataService dataService = new BooksDataService();
Dictionary <string, double> pairs = new Dictionary <string, double>();
List <Book> books = new List <Book>();
IDataLoader dataLoader = new CSVDataLoader();
BookDetails bookDetails = new BookDetails();
bookDetails = dataService.GetBookDetails();
Dictionary <string, List <long> > keyValuePairs = new Dictionary <string, List <long> >();
IRatingAggregator aggregator = new RatingAggregatorClass();
keyValuePairs = aggregator.Aggregate(bookDetails, preference);
IRecommender recommender = new PearsonRecommender();
List <long> baseData = new List <long>();
foreach (BookUserRating rating in bookDetails.bookUserRatings)
{
if (string.Compare(rating.ISBN, preference.ISBN) == 0)
{
baseData.Add(rating.BookRating);
}
}
foreach (var keyValue in keyValuePairs)
{
double corValue = recommender.GetCorrelation(baseData, keyValue.Value);
pairs.Add(keyValue.Key, corValue);
}
pairs = pairs.OrderByDescending(pair => pair.Value).ToDictionary(pair => pair.Key, pair => pair.Value);
int count = 0;
foreach (var item in pairs)
{
if (count == limit)
{
break;
}
count++;
books.Add(bookDetails.books.Find(b => b.ISBN == item.Key));
}
return(books);
}
