Methods that calculate dense vector representations for features in unstructured data-such as words in a document-have proven to be very successful for knowledge representation. We study how to estimate dense representations when multiple feature types exist within a dataset for supervised learning where explicit labels are available, as well as for unsupervised learning where there are no labels. Feat2Vec calculates embeddings for data with multiple feature types enforcing that all different feature types exist in a common space. In the supervised case, we show that our method has advantages over recently proposed methods; such as enabling higher prediction accuracy, and providing a way to avoid the cold-start problem. In the unsupervised case, our experiments suggest that Feat2Vec significantly outperforms existing algorithms that do not leverage the structure of the data. We believe that we are the first to propose a method for learning unsupervised embeddings that leverage the structure of multiple feature types.
SUMMARY.
The paper presents an extension of word2vec for structured features.
The authors introduced a new compatibility function between features and, as in the skipgram approach, they propose a variation of negative sampling to deal with structured features.
The learned representation of features is tested on a recommendation-like task.
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OVERALL JUDGMENT
The paper is not clear and thus I am not sure what I can learn from it.
From what is written on the paper I have trouble to understand the definition of the model the authors propose and also an actual NLP task where the representation induced by the model can be useful.
For this reason, I would suggest the authors make clear with a more formal notation, and the use of examples, what the model is supposed to achieve.
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DETAILED COMMENTS
When the authors refer to word2vec is not clear if they are referring to skipgram or cbow algorithm, please make it clear.
Bottom of page one: "a positive example is 'semantic'", please, use another expression to describe observable examples, 'semantic' does not make sense in this context.
Levi and Goldberg (2014)  do not say anything about factorization machines, could the authors clarify this point?
Equation (4), what do i and j stand for? what does \beta represent? is it the embedding vector? How is this formula related to skipgram or cbow?
The introduction of structured deep-in factorization machine should be more clear with examples that give the intuition on the rationale of the model.
The experimental section is rather poor, first, the authors only compare themselves with word2ve (cbow), it is not clear what the reader should learn from the results the authors got.
Finally, the most striking flaw of this paper is the lack of references to previous works on word embeddings and feature representation, I would suggest the author check and compare themselves with previous work on this topic.