snippet.cs

// load word2vec model
_vocabulary = new Word2VecBinaryReader().Read(HostingEnvironment.MapPath("~/App_Data/GoogleNews-vectors-negative300-SLIM.bin"));

// ... collect all words in post (words is List<string>, not included as implementation specific) ...

double[] vector = new double[_vocabulary.VectorDimensionsCount];

// add all words that exist in the vocabulary
int inVocabularyCount = 0;
foreach(string word in words)
{
    try
    {
        float[] wordVector = _vocabulary.GetRepresentationFor(word).NumericVector;
        for (int i = 0; i < _vocabulary.VectorDimensionsCount; i++)
        {
            vector[i] += (double)wordVector[i];
        }
        inVocabularyCount++;
    }
    catch
    {
        // ignore anything not in vocabulary
    }
}

// return the average of the vectors 
for (int i = 0; i < _vocabulary.VectorDimensionsCount; i++)
{
    vector[i] /= inVocabularyCount;
}

return vector;

// ... once all posts have a vector we just find the closest ones, compare this post to others and sort the list by distance ...
double d = Distance.Cosine(vectorThisPost, vectorOtherPost);