/// <summary>
/// Infers parameters for a given cold user.
/// </summary>
/// <param name="userFeatures">The user features.</param>
/// <returns>A distribution over the user parameters.</returns>
public UserParameterDistribution InferUserParameters(SparseFeatureVector userFeatures)
{
UserParameterDistribution result;
if (this.userFeatureParameterPosteriors.FeatureCount == 0)
{
Debug.Assert(
userFeatures.FeatureCount == 0,
"The number of user features passed must be equal to the number of user features learned.");
result = this.userParameterDistributionAverage;
}
else
{
GaussianArray traits;
Gaussian bias;
AlgorithmUtils.AddFeatureContribution(
this.userParameterDistributionAverage,
this.userFeatureParameterPosteriors,
userFeatures,
out traits,
out bias);
result = new UserParameterDistribution(traits, bias, this.userParameterDistributionAverage.Thresholds);
}
return(result);
}
/// <summary>
/// Sets up the inference algorithm to operate on a single user given the posteriors over the parameters of the user.
/// </summary>
/// <param name="userParameterPosteriors">The posteriors over the parameters of the user.</param>
private void SetupUserFromPosteriors(UserParameterDistribution userParameterPosteriors)
{
Debug.Assert(userParameterPosteriors != null, "Valid user parameter posteriors must be provided.");
this.inferenceAlgorithm.UserCount = 1;
this.inferenceAlgorithm.UserThresholdCount = userParameterPosteriors.Thresholds.Count;
this.inferenceAlgorithm.UserTraitsPrior = new GaussianMatrix(new[] { userParameterPosteriors.Traits });
this.inferenceAlgorithm.UserBiasPrior = new GaussianArray(new[] { userParameterPosteriors.Bias });
this.inferenceAlgorithm.UserThresholdsPrior = new GaussianMatrix(new[] { userParameterPosteriors.Thresholds });
}
/// <summary>
/// Initializes a new instance of the <see cref="ColdUserItemParameterAlgorithm"/> class.
/// </summary>
/// <param name="userFeatureParameterPosteriors">The posteriors over the user feature related parameters learned during community training.</param>
/// <param name="itemFeatureParameterPosteriors">The posteriors over the item feature related parameters learned during community training.</param>
/// <param name="userParameterDistributionAverage">The average trait, bias, and threshold posterior over all users in training.</param>
/// <param name="itemParameterDistributionAverage">The average trait and bias posterior over all items in training.</param>
public ColdUserItemParameterAlgorithm(
FeatureParameterDistribution userFeatureParameterPosteriors,
FeatureParameterDistribution itemFeatureParameterPosteriors,
UserParameterDistribution userParameterDistributionAverage,
ItemParameterDistribution itemParameterDistributionAverage)
{
this.userFeatureParameterPosteriors = userFeatureParameterPosteriors;
this.itemFeatureParameterPosteriors = itemFeatureParameterPosteriors;
this.userParameterDistributionAverage = userParameterDistributionAverage;
this.itemParameterDistributionAverage = itemParameterDistributionAverage;
}
/// <summary>
/// Infers the distribution over the rating which a given user will give to an item.
/// </summary>
/// <param name="userParameterPosteriors">The posteriors over user parameters.</param>
/// <param name="itemParameterPosteriors">The posteriors over item parameters.</param>
/// <returns>The distribution over the rating.</returns>
public Discrete InferRatingDistribution(
UserParameterDistribution userParameterPosteriors, ItemParameterDistribution itemParameterPosteriors)
{
Debug.Assert(userParameterPosteriors != null && itemParameterPosteriors != null, "A valid posteriors must be provided.");
Debug.Assert(
userParameterPosteriors.Traits.Count == itemParameterPosteriors.Traits.Count,
"Given posteriors should have the same associated number of traits.");
this.inferenceAlgorithm.TraitCount = userParameterPosteriors.Traits.Count;
this.SetupUserFromPosteriors(userParameterPosteriors);
this.SetupItemFromPosteriors(itemParameterPosteriors);
this.inferenceAlgorithm.ObservationCount = 1;
this.inferenceAlgorithm.UserIds = new[] { 0 };
this.inferenceAlgorithm.ItemIds = new[] { 0 };
this.inferenceAlgorithm.Execute(1);
return(this.inferenceAlgorithm.RatingsMarginal()[0]);
}