protected override Pair ProcessRatings(IRating firstRating, IRating secondRating)
{
var pair = new Pair()
{
FirstRating = firstRating, SecondRating = secondRating
};
if (firstRating.Subject.Id == secondRating.Subject.Id)
{
++IndexFirst;
++IndexSecond;
}
else if (firstRating.Subject.Id < secondRating.Subject.Id)
{
var rating = new SimpleRating(firstRating.Rater, firstRating.Subject, _defaultValue);
pair.FirstRating = rating;
++IndexFirst;
}
else
{
var rating = new SimpleRating(secondRating.Rater, secondRating.Subject, _defaultValue);
pair.SecondRating = rating;
++IndexSecond;
}
return(pair);
}
private IEnumerable <Pair> GetRatingTail(ref int index, int count, IEnumerable <IRating> ratings, IRater rater, bool isSecondFake)
{
var list = new LinkedList <Pair>();
while (index < count)
{
var rating = ratings.ElementAt(index);
var ratingFake = new SimpleRating(rater, rating.Subject, _defaultValue);
var pair = isSecondFake
? new Pair()
{
FirstRating = rating, SecondRating = ratingFake
}
: new Pair()
{
FirstRating = ratingFake, SecondRating = rating
};
++index;
list.AddLast(pair);
}
return(list);
}
public virtual IRating RecommendSubject(IRater rater, ISubject subject)
{
var alreadyRated = Ratings.Find(p => p.Rater.Id == rater.Id && p.Subject.Id == subject.Id);
if (alreadyRated != null)
{
return(alreadyRated);
}
var ratings = Ratings.Where(p => p.Subject.Id == subject.Id && p.Rater.Id != rater.Id).ToList();
if (!ratings.Any())
{
return(null);
}
var meanVote = RatersMeanVote(rater);
var options = new ParallelOptions()
{
MaxDegreeOfParallelism = Environment.ProcessorCount
};
var sumBag = new ConcurrentBag <decimal>();
var weightSumBag = new ConcurrentBag <decimal>();
Parallel.ForEach(ratings, options, rating =>
{
try
{
var weight = Weight(rater, rating.Rater);
var mean = RatersMeanVote(rating.Rater);
var value = (decimal)rating.Value;
var diff = value - mean;
var val = weight * diff;
weightSumBag.Add(weight);
sumBag.Add(val);
}
catch (Exception) { }
});
var sum = sumBag.Sum();
var weightSum = weightSumBag.Sum();
if (weightSum == 0)
{
return(null);
}
var kappa = 1 / weightSum;
var result = meanVote + kappa * sum;
var res = (double)result;
var rate = new SimpleRating(rater, subject, res);
return(rate);
}