public IEnumerable <IRating> RecommendSubjects(IRater rater, IEnumerable <ISubject> subjects, int take = -1, int skip = 0)
{
var cachedResults = new ConcurrentBag <IRating>();
var notCachedSubjects = new ConcurrentBag <ISubject>();
var options = new ParallelOptions()
{
MaxDegreeOfParallelism = Environment.ProcessorCount
};
Parallel.ForEach(subjects, options, subject =>
{
var key = CreateCacheKey(rater, subject);
var value = 0D;
if (_cache.TryGetValue(key, out value))
{
var ranking = new SimpleRating(rater, subject, value);
cachedResults.Add(ranking);
}
else
{
notCachedSubjects.Add(subject);
}
});
var partial = _algorithm.RecommendSubjects(rater, notCachedSubjects);
var results = partial.ToList();
results.AddRange(cachedResults);
var sorted = results.OrderByDescending(p => p.Value).AsEnumerable();
if (skip > 0)
{
sorted = sorted.Skip(skip);
}
if (take > 0)
{
sorted = sorted.Take(take);
}
return(sorted);
}
public override double CalculateError(IRecommendation recommendation, IEnumerable <IRating> ratings, IEnumerable <IRater> raters, IEnumerable <ISubject> subjects)
{
double error = 0.0;
foreach (var rater in raters)
{
var topList = recommendation.RecommendSubjects(rater, subjects, 0, 10);
foreach (var result in topList)
{
if (!ratings.Any(r => r.Subject == result.Subject && r.Rater == result.Rater))
{
error += 1.0;
}
}
}
return(error);
}
