Hi!
First of all thanks for the framework :) I would like to use it to benchmark different models allowing the use item vector features (i.e. FeatureModality).
By following this tutorial I was able to train a TextModality-based model with a FeatureModality, but by looking at the code I have the impression that the feature vectors are not taken "as they are" but further processed as text, i.e. by computing a Bag of Words representation of the features, and using this BoW as input to the model, which is not what I want. Is there a way to use FeatureModality for models that use GraphModality, TextModality, ImageModality, or SentimentModality, leaving the FeatureModality vectors as they are, without further pre-processing happening under the hood?
Alternatively: Is there a list of all the models implemented in cornac that leverage item vector features as FeatureModality (in the same way as BiVAECF, for instance)?
Cheers!
Marta
Hi!
First of all thanks for the framework :) I would like to use it to benchmark different models allowing the use item vector features (i.e.
FeatureModality).By following this tutorial I was able to train a
TextModality-based model with aFeatureModality, but by looking at the code I have the impression that the feature vectors are not taken "as they are" but further processed as text, i.e. by computing a Bag of Words representation of the features, and using this BoW as input to the model, which is not what I want. Is there a way to useFeatureModalityfor models that useGraphModality,TextModality,ImageModality, orSentimentModality, leaving theFeatureModalityvectors as they are, without further pre-processing happening under the hood?Alternatively: Is there a list of all the models implemented in cornac that leverage item vector features as
FeatureModality(in the same way as BiVAECF, for instance)?Cheers!
Marta