/// <summary>
/// A simple recommendation algorithm that starts from a "user" and examines movies the user has seen filtered by
/// the {@code minRating} which removes movies that hold a rating lower than the value specified. It then samples
/// the actors in the movies the user has seen and uses that to find other movies those actors have been in that
/// the user has not yet seen. Those movies are grouped, counted and sorted based on that count to produce the
/// recommendation.
/// </summary>
public static GraphTraversal <Vertex, Vertex> Recommend(this GraphTraversal <Vertex, Vertex> t, int recommendations,
int minRating, Recommender recommender, GraphTraversal <object, object> include)
{
if (recommendations <= 0)
{
throw new ArgumentException("recommendations must be greater than zero");
}
return(t.Rated(minRating, 0).
Aggregate("seen").
Local <List <Vertex> >(RecommenderLookup.Traversals[recommender]).
Unfold <Vertex>().In(EdgeActor).
Where(Without(new List <string> {
"seen"
})).
Where(include).
GroupCount <Vertex>().
Order(Local).
By(Values, Decr).
Limit <IDictionary <Vertex, long> >(Local, recommendations).
Select <Vertex>(Keys).
Unfold <Vertex>());
}
